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+++ b/README.md
@@ -1,4 +1,16 @@
-# DL4MATH - Reading List
+# Deep Learning for Mathematical Reasoning (DL4MATH)
+
+[![Awesome](https://awesome.re/badge.svg)](https://github.com/lupantech/dl4math) 
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![Survey](https://img.shields.io/badge/Survey-DL4MATH-blue)](https://github.com/lupantech/dl4math) 
+
+This repository is the reading list on ***Deep Learning for Mathematical Reasoning (DL4MATH)***. 
+
+**Contributors**: [Pan Lu](https://lupantech.github.io/) @UCLA, [Liang Qiu](https://www.lqiu.info/) @UCLA, [Wenhao Yu](https://wyu97.github.io/) @Notre Dame, [Kai-Wei Chang](http://web.cs.ucla.edu/~kwchang/) @UCLA, [Sean Welleck](https://wellecks.com/) @UW
+
+For more details, please refer to the paper: [A Survey of Deep Learning for Mathematical Reasoning](https://arxiv.org/abs/2212.10535).
+
+:bell: If you have any suggestions or notice something we missed, please don't hesitate to let us know. You can directly email Pan Lu (lupantech@gmail.com), comment on the [twitter](https://twitter.com/lupantech/status/1605400505697841155), or post a issue on this repo.
 
 
 
@@ -11,17 +23,25 @@
 - **Representing Numbers in NLP: a Survey and a Vision**, NACL 2021 [[paper](https://aclanthology.org/2021.naacl-main.53/)]
 - **Survey on Mathematical Word Problem Solving Using Natural Language Processing**, ICIICT 2021 [[paper](https://ieeexplore.ieee.org/abstract/document/8741437)]
 - **A Survey in Mathematical Language Processing**, arXiv:2205.15231 [[paper](https://arxiv.org/abs/2205.15231)]
-- **Partial Differential Equations Meet Deep Neural Networks**: A Survey, arXiv:2211.05567 [[paper](https://arxiv.org/abs/2211.05567)]
+- **Partial Differential Equations Meet Deep Neural Networks: A Survey**, arXiv:2211.05567 [[paper](https://arxiv.org/abs/2211.05567)]
+- :fire: **Reasoning with Language Model Prompting: A Survey**, arXiv:2212.09597 [[paper](https://arxiv.org/abs/2212.09597)]
+- :fire: **Towards Reasoning in Large Language Models**: arXiv:2212.10403 [[paper](https://arxiv.org/abs/2212.10403)]
+- :fire: **A Survey for In-context Learning**, arXiv:2301.00234 [[paper](https://arxiv.org/abs/2301.00234)]
+
+### Related Blogs
+
+- :fire: **How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources**, Dec 2022, Yao Fu’s Notion [[link](https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tracing-Emergent-Abilities-of-Language-Models-to-their-Sources-b9a57ac0fcf74f30a1ab9e3e36fa1dc1)]
 
 ### Workshops
 
-- **The 1st MATH-AI Workshop: the Role of Mathematical Reasoning in General Artificial Intelligence**, ICLR 2021 [[website](https://mathai-iclr.github.io/)]
-- **Math AI for Education: Bridging the Gap Between Research and Smart Education (MATHAI4ED)]**, NeurIPS 2021 [[website](https://mathai4ed.github.io/)]
-- **The 1st Workshop on Mathematical Natural Language Processing**, EMNLP 2022 [[website](https://sites.google.com/view/1st-mathnlp/)]
+- :fire: **The 1st MATH-AI Workshop: the Role of Mathematical Reasoning in General Artificial Intelligence**, ICLR 2021 [[website](https://mathai-iclr.github.io/)]
+- :fire: **Math AI for Education: Bridging the Gap Between Research and Smart Education (MATHAI4ED)**, NeurIPS 2021 [[website](https://mathai4ed.github.io/)]
+- :fire: **The 1st Workshop on Mathematical Natural Language Processing**, EMNLP 2022 [[website](https://sites.google.com/view/1st-mathnlp/)]
 - :fire: **The 2nd MATH-AI Workshop: Toward Human-Level Mathematical Reasoning**, NeurIPS 2022  [[website](https://mathai2022.github.io/)]
 
 ### Talks
 
+- **Can GPT-3 do math? | Grant Sanderson and Lex Fridman**, 2020 [[YouTube](https://www.youtube.com/watch?v=TMxAbNAVrzI&ab_channel=LexClips)]
 - **Computer Scientist Explains One Concept in 5 Levels of Difficulty**, 2022 [[YouTube](https://www.youtube.com/watch?v=fOGdb1CTu5c)]
 
 
@@ -30,7 +50,7 @@
 
 ### Math Word Problems (MWP)
 
-- [AI2] **Learning to Solve Arithmetic Word Problems with Verb Categorization**, EMNLP 2014 [[paper](https://aclanthology.org/D14-1058/)]
+- [AI2/Verb395] **Learning to Solve Arithmetic Word Problems with Verb Categorization**, EMNLP 2014 [[paper](https://aclanthology.org/D14-1058/)]
 - [Alg514] **Learning to automatically solve algebra word problems**, ACL 2014 [[paper](https://aclanthology.org/P14-1026/)]
 - [IL] **Reasoning about Quantities in Natural Language**, TACL 2015 [[paper](https://aclanthology.org/Q15-1001/)]
 - [SingleEQ] **Parsing Algebraic Word Problems into Equations**, TACL 2015 [[paper](https://aclanthology.org/Q15-1042/)]
@@ -40,205 +60,274 @@
 - [MAWPS] **MAWPS: A math word problem repository**, NAACL-HLT 2016 [[paper](https://aclanthology.org/N16-1136/)]
 - [AllArith] **Unit dependency graph and its application to arithmetic word problem solving**, AAAI 2017 [[paper](https://arxiv.org/abs/1612.00969)]
 - [DRAW-1K] **Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems**, ACL 2017 [[paper](https://aclanthology.org/E17-1047/)]
-- [Math23K] **Deep neural solver for math word problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1088/)]
-- [AQuA-RAT] **Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems**, ACL 2017 [[paper](https://arxiv.org/abs/1705.04146)]
+- :fire: [Math23K] **Deep neural solver for math word problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1088/)]
+- [AQuA] **Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems**, ACL 2017 [[paper](https://arxiv.org/abs/1705.04146)]
 - [Aggregate] **Mapping to Declarative Knowledge for Word Problem Solving**, TACL 2018 [[paper](https://arxiv.org/abs/1712.09391)]
-- [MathQA] **MathQA: Towards interpretable math word problem solving with operation-based formalisms**, NAACL-HLT 2019 [[paper](https://aclanthology.org/N19-1245/)]
+- :fire: [MathQA] **MathQA: Towards interpretable math word problem solving with operation-based formalisms**, NAACL-HLT 2019 [[paper](https://aclanthology.org/N19-1245/)]
 - [ASDiv] **A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers**, ACL 2020 [[paper](https://arxiv.org/abs/2106.15772)]
+- [HMWP] **Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems**, EMNLP 2020 [[paper](https://arxiv.org/abs/2010.06823)]
 - [Ape210K] **Ape210k: A large-scale and template-rich dataset of math word problems**, arXiv:2009.11506 [[paper](https://arxiv.org/abs/2009.11506)]
-- [SVAMP] **Are NLP Models really able to Solve Simple Math Word Problems?**, NAACL-HIT 2021 [[paper](https://arxiv.org/abs/2103.07191)]
-- :fire: [IconQA] **IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning**, NeurIPS 2021 (Datasets and Benchmarks)] [[paper](https://arxiv.org/abs/2110.13214)]
+- :fire: [SVAMP] **Are NLP Models really able to Solve Simple Math Word Problems?**, NAACL-HIT 2021 [[paper](https://arxiv.org/abs/2103.07191)]
 - :fire: [GSM8K] **Training verifiers to solve math word problems**, arXiv:2110.14168 [[paper](https://arxiv.org/abs/2110.14168)]
-- [MathQA-Python] **Program synthesis with large language models**, arXiv:2108.07732 [[paper](https://arxiv.org/abs/2108.07732)]
+- :fire: [IconQA] **IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning**, NeurIPS 2021] [[paper](https://arxiv.org/abs/2110.13214)]
+- :fire: [MathQA-Python] **Program synthesis with large language models**, arXiv:2108.07732 [[paper](https://arxiv.org/abs/2108.07732)]
 - [ArMATH] **ArMATH: a Dataset for Solving Arabic Math Word Problems**, LREC 2022 [[paper](https://aclanthology.org/2022.lrec-1.37/)]
 - :fire: [TabMWP] **Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**, arXiv:2209.14610, 2022 [[paper](https://arxiv.org/abs/2209.14610)]
 
+### Theorem Proving (TP)
+
+- [MML] **Four Decades of Mizar**, Journal of Automated Reasoning 2015, [[paper](https://dl.acm.org/doi/abs/10.1007/s10817-015-9345-1)]
+- [HolStep] **HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving**, ICLR 2017 [[paper](https://arxiv.org/abs/1703.00426)]
+- [GamePad] **GamePad: A Learning Environment for Theorem Proving**, ICLR 2019 [[paper](https://arxiv.org/abs/1806.00608)]
+- :fire: [CoqGym] **Learning to Prove Theorems via Interacting with Proof Assistants**, ICML 2019 [[paper](https://arxiv.org/abs/1905.09381)]
+- [HOList] **HOList: An environment for machine learning of higher order logic theorem proving**, ICML 2019 [[paper](https://arxiv.org/abs/1904.03241)]
+- [IsarStep] **IsarStep: a Benchmark for High-level Mathematical Reasoning**, ICLR 2021 [[paper](https://arxiv.org/abs/2006.09265)]
+- [LISA] **LISA: Language models of ISAbelle proofs**, AITP 2021 [[paper](http://aitp-conference.org/2021/abstract/paper_17.pdf)]
+- [INT] **INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving**, ICLR 2021 [[paper](https://arxiv.org/abs/2007.02924)]
+- :fire: [NaturalProofs] **NaturalProofs: Mathematical Theorem Proving in Natural Language**, NeurIPS 2021 [[paper](https://arxiv.org/abs/2104.01112)]
+- [NaturalProofs-Gen] **NaturalProver: Grounded Mathematical Proof Generation with Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.12910)]
+- :fire: [MiniF2F] **MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics**, ICLR 2022 [[paper](https://arxiv.org/abs/2109.00110)]
+- :fire: [LeanStep] **Proof Artifact Co-training for Theorem Proving with Language Models**, ICLR 2022 [[paper](https://arxiv.org/abs/2102.06203)]
+- :fire: [miniF2F+informal] **Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs**, arXiv:2210.12283 [[paper](https://arxiv.org/abs/2210.12283)]
+
 ### Geometry Problem Solving (GPS)
 
-- [GEOS] **Solving geometry problems: Combining text and diagram interpretation**, EMNLP 2015 [[paper](https://aclanthology.org/D15-1171/)]
+- :fire: [GEOS] **Solving geometry problems: Combining text and diagram interpretation**, EMNLP 2015 [[paper](https://aclanthology.org/D15-1171/)]
 - [GeoShader] **Synthesis of solutions for shaded area geometry problems**, The Thirtieth International Flairs Conference, 2017 [[paper](https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/viewFile/15416/14902)]
-- [GEOS-OS] **Learning to solve geometry problems from natural language demonstrations in textbooks**, Proceedings of the 6th Joint Conference on Lexical and Computational Semantics, 2017 [[paper](https://aclanthology.org/S17-1029/)]
 - [GEOS++] **From textbooks to knowledge: A case study in harvesting axiomatic knowledge from textbooks to solve geometry problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1081/)]
-- [GeoQA] **GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning**, Findings of ACL 2021 [[paper](https://aclanthology.org/2021.findings-acl.46.pdf)]
+- [GEOS-OS] **Learning to solve geometry problems from natural language demonstrations in textbooks**, Proceedings of the 6th Joint Conference on Lexical and Computational Semantics, 2017 [[paper](https://aclanthology.org/S17-1029/)]
 - :fire: [Geometry3K] **Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning**, ACL 2021 [[paper](https://aclanthology.org/2021.acl-long.528/)]
+- [GeoQA] **GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning**, Findings of ACL 2021 [[paper](https://aclanthology.org/2021.findings-acl.46.pdf)]
+- [GeoQA+] **An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding**, COLING 2022 [[paper](https://aclanthology.org/2022.coling-1.130/)]
 - :fire: [UniGeo] **UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression**, EMNLP 2022 [[paper](https://lupantech.github.io/papers/emnlp22_unigeo.pdf)]
-- [GeoRE] **GeoRE: A Relation Extraction Dataset for Chinese Geometry Problems**, NeurIPS 2021 MATHAI4ED Workshop [[paper](https://mathai4ed.github.io/papers/papers/paper_6.pdf)]
-- [GeoQA+] **An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding**, ICCL 2022 [[paper](https://aclanthology.org/2022.coling-1.130/)]
-
-### Theorem Proving (TP)
-
-- [HOList] **HOList: An environment for machine learning of higher order logic theorem proving**, ICML 2019 [[paper](https://arxiv.org/abs/1904.03241)]
-- [INT] **INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving**, ICLR 2021 [[paper](https://arxiv.org/abs/2007.02924)]
-- :fire: **NaturalProofs: Mathematical Theorem Proving in Natural Language**, NeurIPS 2021 (Datasets and Benchmarks) [[paper](https://arxiv.org/abs/2104.01112)]
-- :fire: [MiniF2F] **MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics**, ICLR 2022 [[paper](https://arxiv.org/abs/2109.00110)]
 
 ### Math Question Answering (MathQA)
 
+- [QUAREL] **QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships**, AAAI 2019 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/4687)] 
+- [McTaco] **“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding**, EMNLP 2019 [[paper](https://aclanthology.org/D19-1332/)]
+- :fire: [DROP] **DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**, NAACL 2019 [[paper](https://aclanthology.org/N19-1246/)]
+- :fire: [Mathematics] **Analysing Mathematical Reasoning Abilities of Neural Models**, ICLR 2019 [[paper](https://arxiv.org/abs/1904.01557)]
+- [FinQA] **FinQA: A Dataset of Numerical Reasoning over Financial Data**, EMNLP 2021 [[paper](https://arxiv.org/abs/2109.00122)]
 - [Fermi] **How Much Coffee Was Consumed During EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI**, EMNLP 2020 [[paper](https://arxiv.org/abs/2110.14207)]
+- :fire: [MATH, AMPS] **Measuring Mathematical Problem Solving With the MATH Dataset**, NeurIPS 2021 [[paper](https://arxiv.org/abs/2103.03874)]
 - [TAT-QA] **TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**, ACL-JCNLP 2021 [[paper](https://arxiv.org/abs/2105.07624)]
-- [FinQA] **FinQA: A Dataset of Numerical Reasoning over Financial Data**, EMNLP 2021 [[paper](https://arxiv.org/abs/2109.00122)]
-- [NumGLUE] **NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.246/)]
 - [MultiHiertt] **MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.454/)]
-- :fire: **Lila: A Unified Benchmark for Mathematical Reasoning**, EMNLP 2022 [[paper](https://arxiv.org/abs/2210.17517)]
+- [NumGLUE] **NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.246/)]
+- :fire: [Lila] **Lila: A Unified Benchmark for Mathematical Reasoning**, EMNLP 2022 [[paper](https://arxiv.org/abs/2210.17517)]
 
-### Other Math Tasks
+### Other Quantitative Problems
 
-- [TextbookQA] **Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension**, CVPR 2017 [[paper](https://ieeexplore.ieee.org/document/8100054)]
-- [Figureqa] **Figureqa: An annotated figure dataset for visual reasoning**, arXiv:1710.07300 [[paper](https://arxiv.org/abs/1710.07300)]
-- [Dvqa] **Dvqa: Understanding data visualizations via question answering**, CVPR 2018 [[paper](https://arxiv.org/abs/1801.08163)]
-- [Raven] **Raven: A dataset for relational and analogical visual reasoning**, CVPR 2019 [[paper](https://arxiv.org/abs/1903.02741)]
+- [FigureQA] **Figureqa: An annotated figure dataset for visual reasoning**, arXiv:1710.07300 [[paper](https://arxiv.org/abs/1710.07300)]
+- :fire: [DVQA] **Dvqa: Understanding data visualizations via question answering**, CVPR 2018 [[paper](https://arxiv.org/abs/1801.08163)]
+- [DREAM] **DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension**,TACL 2019 [[paper](https://arxiv.org/abs/1902.00164)]
+- [EQUATE] **EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**, CoNLL 2019 [[paper](https://arxiv.org/abs/1901.03735)]
+- :fire: [NumerSense] **Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models**, EMNLP 2020 [[paper](https://arxiv.org/abs/2005.00683)]
 - [MNS] **Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning**, AAAI 2020 [[paper](https://arxiv.org/abs/2004.12193)]
-- [P3] **Programming Puzzles**, NeurIPS 2021 (Datasets and Benchmarks) [[paper](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/3988c7f88ebcb58c6ce932b957b6f332-Abstract-round1.html)]
-- [IsarStep] **IsarStep: a Benchmark for High-level Mathematical Reasoning**, ICLR 2021 [[paper](https://arxiv.org/abs/2006.09265)]
-- [PhysNLU] **PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics**, 2022 [[paper](https://arxiv.org/abs/2201.04275)]
-- [ScienceQA] **Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2209.09513)]
-- [PGDP5K] **PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems**, arXiv:2205.0994 [[paper](https://arxiv.org/abs/2205.09947)]
+- [P3] **Programming Puzzles**, NeurIPS 2021 [[paper](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/3988c7f88ebcb58c6ce932b957b6f332-Abstract-round1.html)]
+- [NOAHQA] **NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset**,  Findings of EMNLP 2021 [[paper](https://arxiv.org/abs/2109.10604)]
 - [ConvFinQA] **ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering**, arXiv:2210.03849 [[paper](https://arxiv.org/abs/2210.03849)]
-- [APPS, code generation] **Measuring Coding Challenge Competence With APPS**, NeurIPS 2021 (Datasets and Benchmarks) [[paper](https://arxiv.org/abs/2105.09938)]
-
-
-
-## 🧩 Neural Networks for Math
-
-### Neural Math Word Problem Solving
-
-- [symbolic reasoning] **Semantic parsing of pre-university math problems**, ACL 2017 [[paper](https://aclanthology.org/P17-1195/)]
-- [Equation templates] **Learning fine-grained expressions to solve math word problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1084/)]
-- [Dependency Graph] **Unit dependency graph and its application to arithmetic word problem solving**, AAAI 2017 [[paper](https://arxiv.org/abs/1612.00969)]
-- **Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems**, ACL 2017 [[paper](https://aclanthology.org/P17-1015/)]
-- [expression tree] **Translating a math word problem to an expression tree**, EMNLP 2018 [[paper](https://aclanthology.org/D18-1132/)]
-- [logical reasoning] **Mapping to declarative knowledge for word problem solving**, TACL 2018 [[paper](https://arxiv.org/abs/1712.09391)]
-- [equation templates] **Template-based math word problem solvers with recursive neural networks**, AAAI 2019 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/4697)]
-- [expression tree] **Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems**, EMNLP 2020 [[paper](https://arxiv.org/abs/2010.06823)]
-- [Weak Supervision] **Learning by Fixing: Solving Math Word Problems with Weak Supervision**, AAAI 2021 [[paper](https://arxiv.org/abs/2012.10582)]
-- **Solving Math Word Problems with Teacher Supervision**, IJCAI 2021 [[paper](https://www.ijcai.org/proceedings/2021/485)]
-- **Analogical Math Word Problems Solving with Enhanced Problem-Solution Association**, EMNLP 2022 [[paper](https://aclanthology.org/2021.emnlp-main.272/)]
-
-### Neural Geometry Solving
-
-- **Synthesis of geometry proof problems**, AAAI 2014 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/8745)]
-- **Diagram understanding in geometry questions**, AAAI 2014 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/9146#:~:text=The%20first%20step%20in%20solving,them%20to%20corresponding%20textual%20descriptions.)]
-- **Retrieving geometric information from images: the case of hand-drawn diagrams**, Data Mining and Knowledge Discovery 2017 [[paper](https://link.springer.com/article/10.1007/s10618-017-0494-1)]
-- **Automatic understanding and formalization of natural language geometry problems using syntax-semantics models**, International Journal of Innovative Computing, Information and Control 2018 [[paper](http://www.ijicic.org/ijicic-140106.pdf)]
-- **A Framework for Solving Explicit Arithmetic Word Problems and Proving Plane Geometry Theorems**, International Journal of Pattern Recognition and Artificial Intelligence 2019 [[paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001419400056)]
-- [Knowledge] **Discourse in multimedia: A case study in extracting geometry knowledge from textbooks**, Computational Linguistics, 2020 [[paper](https://aclanthology.org/J19-4002/)]
-
-### Neural Theorem Proving
-
-- **DeepMath - Deep Sequence Models for Premise Selection**, NeurIPS 2016 [[paper](https://arxiv.org/abs/1606.04442)]
-- **Deep network guided proof search**, arXiv:1701.06972 [[paper](https://arxiv.org/abs/1701.06972)]
-- **Graph representations for higher-order logic and theorem proving**, AAAI 2020 [[paper](https://ojs.aaai.org//index.php/AAAI/article/view/5689)]
-- **Neural Theorem Proving on Inequality Problems**, AITP 2020 [[paper](http://aitp-conference.org/2020/abstract/paper_18.pdf)]
-- **Latent Action Space for Efficient Planning in Theorem Proving**, 2021 [[paper](http://aitp-conference.org/2021/abstract/paper_24.pdf)]
-- **Learning to Give Checkable Answers with Prover-Verifier Games**, arXiv:2108.12099 [[paper](https://arxiv.org/abs/2108.12099)]
-- **REFACTOR: Learning to Extract Theorems from Proofs**, 2022 [[paper](https://openreview.net/forum?id=827jG3ahxL)]
-
-### Neural Networks for MathQA
-
-- **Combining retrieval, statistics, and inference to answer elementary science questions**, AAAI 2016 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/10325)] 
-
-### Neural Networks for Other Math Tasks
-
-- :fire: **Advancing mathematics by guiding human intuition with AI**, Nature 2021 [[paper](https://www.nature.com/articles/s41586-021-04086-x)]
-- **Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics**, AAAI 2022 [[paper](https://arxiv.org/abs/2109.13986)]
-- :fire: **Discovering faster matrix multiplication algorithms with reinforcement learning**, Nature 2022 [[paper](https://www.nature.com/articles/s41586-022-05172-4)]
-
-
-
-## 📜 Pre-trained Models for Math
-
-### Pre-trained Language Models (PTLMs)
-
-- [GPT-2] **Language models are unsupervised multitask learners**, 2019 [[paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)]
-- [UnifiedQA] **UNIFIEDQA: Crossing Format Boundaries with a Single QA System**, EMNLP 2020 [[paper](https://arxiv.org/abs/2005.00700)]
-
-### Language Models for MWPs
-
-- **Lime: Learning inductive bias for primitives of mathematical reasoning**, ICML 2021 [[paper](https://arxiv.org/abs/2101.06223)]
-- :fire: [IconQA] **IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning**, NeurIPS 2021 (Datasets and Benchmarks)] [[paper](https://arxiv.org/abs/2110.13214)]
-- **MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving**, Findings of NAACL 2022 [[paper](https://arxiv.org/abs/2107.13435)]
-- **TAPEX: Table Pre-training via Learning a Neural SQL Executor**, ICLR 2022 [[paper](https://arxiv.org/abs/2107.07653)]
-- **Insights into Pre-training via Simpler Synthetic Tasks**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2206.10139)]
-- **Learning from Self-Sampled Correct and Partially-Correct Programs**, arXiv:2205.14318 [[paper](https://arxiv.org/abs/2205.14318)]
-- **Solving quantitative reasoning problems with language models**, arXiv:2206.14858 [[paper](https://arxiv.org/abs/2206.14858)]
-
-### Language Models for Geometry Solvers
-
+- [PGDP5K] **PGDP5K: A Diagram Parsing Dataset for Plane Geometry Problems**, arXiv:2205.0994 [[paper](https://arxiv.org/abs/2205.09947)]
+- [GeoRE] **GeoRE: A Relation Extraction Dataset for Chinese Geometry Problems**, NeurIPS 2021 MATHAI4ED Workshop [[paper](https://mathai4ed.github.io/papers/papers/paper_6.pdf)]
+- :fire: [ScienceQA] **Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2209.09513)]
+
+
+
+## 🧩 Neural Networks for Mathematical Reasoning
+
+### General Neural Networks
+
+- [LSTM] **Long short-term memory**, Neural computation 1997 [[paper](https://ieeexplore.ieee.org/abstract/document/6795963)]
+- [Seq2Seq] **Sequence to sequence learning with neural networks**, NeurIPS 2014 [[paper](https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html)]
+- [GRU] **Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation**, EMNLP 2014 [[paper](https://arxiv.org/abs/1406.1078)]
+- [Attention] **Neural machine translation by jointly learning to align and translate**, arXiv:1409.0473 [[paper](https://arxiv.org/abs/1409.0473)]
+- [Attention] **Show, attend and tell: Neural image caption generation with visual attention**, ICML 2015 [[paper](https://arxiv.org/abs/1502.03044)]
+- [Faster-RCNN] **Faster r-cnn: Towards real-time object detection with region proposal networks**, NeurIPS 2015 [[paper](https://arxiv.org/abs/1506.01497)]
+- [TreeLSTM] **Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks**, ACL 2015 [[paper](https://arxiv.org/abs/1503.00075)]
+- [BiLSTM] **Google's neural machine translation system: Bridging the gap between human and machine translation**, arXiv:1609.08144 [[paper](https://arxiv.org/abs/1609.08144)]
+- [ResNet] **Deep residual learning for image recognition**, CVPR 2016 [[paper](https://arxiv.org/abs/1512.03385)]
+- [ConvS2S] **Convolutional sequence to sequence learning**, ICML 2017 [[paper](https://arxiv.org/abs/1705.03122)]
+- [Top-Down Attention] **Bottom-up and top-down attention for image captioning and visual question answering**, CVPR 2018 [[paper](https://arxiv.org/abs/1707.07998)]
+- [FiLM] **Film: Visual reasoning with a general conditioning layer**, AAAI 2018 [[paper](https://arxiv.org/abs/1709.07871)]
+- [BAN] **Bilinear Attention Networks**, NeurIPS 2018 [[paper](https://arxiv.org/abs/1805.07932)]
+- [DAFA] **Dynamic Fusion With Intra-and Inter-Modality Attention Flow for Visual Question Answering**, CVPR 2018 [[paper](https://arxiv.org/abs/1812.05252)]
+
+### Seq2Seq Networks for Math
+
+- :fire: [DNS] **Deep Neural Solver for Math Word Problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1088/)]
+- :fire: [AnsRat] **Program induction by rationale generation: Learning to solve and explain algebraic word problems**, ACL 2017 [[paper](https://arxiv.org/abs/1705.04146)]
+- [Math-EN] **Translating a Math Word Problem to a Expression Tree**, EMNLP 2018 [[paper](https://arxiv.org/abs/1811.05632)]
+- [CASS] **Neural math word problem solver with reinforcement learning**, COLING 2018 [[paper](https://aclanthology.org/C18-1018/)]
+- [SelfAtt] **Data-driven methods for solving algebra word problems**, arXiv:1804.10718 [[paper](https://arxiv.org/abs/1804.10718)]
+- [S-Aligned] **Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems**, NAACL 2019 [[paper](https://aclanthology.org/N19-1272/)]
+- [T-RNN] **Template-based math word problem solvers with recursive neural networks**, AAAI 2019 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/4697)]
+- [GROUP-ATT] **Modeling intra-relation in math word problems with different functional multi-head attentions**, ACL 2019 [[paper](https://aclanthology.org/P19-1619/)]
+- [QuaSP+] **QUAREL: A Dataset and Models for Answering Questions about Qualitative Relationships**, AAAI 2019 [[paper](https://arxiv.org/abs/1811.08048)]
+- [SMART] **SMART: A Situation Model for Algebra Story Problems via Attributed Grammar**, AAAI 2021 [[paper](https://arxiv.org/abs/2012.14011)]
+
+### Graph-based Networks for Math
+
+- [AST-Dec] **Tree-structured decoding for solving math word problems**, EMNLP 2019 [[paper](https://aclanthology.org/D19-1241/)]
+- :fire: [GTS] **A Goal-Driven Tree-Structured Neural Model for Math Word Problems**, IJCAI 2019 [[paper](https://www.ijcai.org/proceedings/2019/736)]
+- [CoqGym] **Learning to Prove Theorems via Interacting with Proof Assistants**, ICML 2019 [[paper](https://arxiv.org/abs/1905.09381)]
+- [KA-S2T] **A knowledge-aware sequence-to-tree network for math word problem solving**, EMNLP 2020 [[paper](https://aclanthology.org/2020.emnlp-main.579/)]
+- [TSN-MD, NT-LSTM] **Solving arithmetic word problems by scoring equations with recursive neural networks**, Expert Systems with Applications 2021 [[paper](https://arxiv.org/abs/2009.05639)]
+- [NS-Solver] **Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks**, ACL 2021 [[paper](https://arxiv.org/abs/2107.01431)]
+- [NumS2T] **Math word problem solving with explicit numerical values**, ACL 2021 [[paper](https://aclanthology.org/2021.acl-long.455/)]
+- [HMS] **Hms: A hierarchical solver with dependency-enhanced understanding for math word problem**, AAAI 2021 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16547)]
+- [LBF] **Learning by fixing: Solving math word problems with weak supervision**, AAAI 2021 [[paper](https://arxiv.org/abs/2012.10582)]
+- [Seq2DAG] **A bottom-up dag structure extraction model for math word problems**, AAAI 2021 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/16075)]
+- [Graph2Tree] **Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem**, EMNLP 2020 [[paper](https://arxiv.org/abs/2004.13781)]
+- [Multi-E/D] **Solving math word problems with multi-encoders and multi-decoders**, COLING 2020 [[paper](https://aclanthology.org/2020.coling-main.262/)]
+- :fire: [Graph2Tree] **Graph-to-Tree Learning for Solving Math Word Problems**, ACL 2020 [[paper](https://aclanthology.org/2020.acl-main.362/)]
+- [EEH-G2T] **An edge-enhanced hierarchical graph-to-tree network for math word problem solving**, EMNLP 2021 [[paper](https://aclanthology.org/2021.findings-emnlp.127/)]
+
+### Other Neural Networks for Math
+
+- [DeepMath] **Deepmath-deep sequence models for premise selection**, NeurIPS 2016 [[paper](https://arxiv.org/abs/1606.04442)]
+- [Holophrasm] **Holophrasm: a neural automated theorem prover for higher-order logic**, arXiv:1608.02644 [[paper](https://arxiv.org/abs/1608.02644)]
+- :fire: [CNNTP, WaveNetTP] **Deep network guided proof search**, arXiv:1701.06972 [[paper](https://arxiv.org/abs/1701.06972)]
+- :fire: [MathDQN] **Mathdqn: Solving arithmetic word problems via deep reinforcement learning**, AAAI 2018 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/11981)]
+- [DDT] **Solving math word problems with double-decoder transformer**, arXiv:1908.10924 [[paper](https://arxiv.org/abs/1908.10924)]
+- [DeepHOL] **HOList: An environment for machine learning of higher order logic theorem proving**, ICML 2019 [[paper](https://arxiv.org/abs/1904.03241)]
+- [NGS] **GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning**, Findings of ACL 2021 [[paper](https://aclanthology.org/2021.findings-acl.46.pdf)]
+- [PGDPNet] **Learning to Understand Plane Geometry Diagram**, NeurIPS 2022 MATH-AI Workshop [[paper](https://mathai2022.github.io/papers/6.pdf)]
+
+
+
+## 📜 Pre-trained Language Models for Mathematical Reasoning
+
+### General Pre-trained Language Models (<100B)
+
+- [Transformer] **Attention is all you need**, NeurIPS 2017 [[paper](https://arxiv.org/abs/1706.03762)]
+- [BERT] **Bert: Pre-training of deep bidirectional transformers for language understanding**, arXiv:1810.04805 [[paper](https://arxiv.org/abs/1810.04805)]
+- [T5] **Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**, JMLR 2020 [[paper](https://arxiv.org/abs/1910.10683)]
+- [RoBERTa] **Roberta: A robustly optimized bert pretraining approach**, arXiv:1907.11692 [[paper](https://arxiv.org/abs/1907.11692)]
+- [GPT-2, 1.5B] **Language models are unsupervised multitask learners**, OpenAI Blog, 2020 [[paper](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)]
+- [BART] **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**, ACL 2020 [[paper](https://arxiv.org/abs/1910.13461)]
+- [ALBERT] **Albert: A lite bert for self-supervised learning of language representations**, arXiv:1909.11942 [[paper](https://arxiv.org/abs/1909.11942)]
+- [GPT-Neo] **The pile: An 800gb dataset of diverse text for language modeling**, arXiv:2101.00027 [[paper](https://arxiv.org/abs/2101.00027)]
+- [VL-T5] **Unifying Vision-and-Language Tasks via Text Generation**, ICML 2021 [[paper](https://arxiv.org/abs/2102.02779)]
+
+### Self-Supervised Learning for Math
+
+- :fire: [GenBERT] **Injecting numerical reasoning skills into language models**, ACL 2020 [[paper](https://arxiv.org/abs/2004.04487)]
+- :fire: [GPT-f] **Generative language modeling for automated theorem proving**, arXiv:2009.03393 [[paper](https://arxiv.org/abs/2009.03393)]
+- [LISA] **LISA: Language models of ISAbelle proofs**, AITP 2021 [[paper](http://aitp-conference.org/2021/abstract/paper_17.pdf)]
+- [MATH-PLM] **Measuring Mathematical Problem Solving With the MATH Dataset**, NeurIPS 2021 [[paper](https://arxiv.org/abs/2103.03874)]
+- [LIME] **Lime: Learning inductive bias for primitives of mathematical reasoning**, ICML 2021 [[paper](https://arxiv.org/abs/2101.06223)]
+- [NF-NSM] **Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models**, arXiv:2112.06109 [[paper](https://arxiv.org/abs/2112.06109)]
+- [MWP-BERT] **MWP-BERT: Numeracy-augmented pre-training for math word problem solving**, Findings of NAACL 2022 [[paper](https://arxiv.org/abs/2107.13435)]
+- [HTPS] **HyperTree Proof Search for Neural Theorem Proving**, arXiv:2205.11491 [[paper](https://arxiv.org/abs/2205.11491)]
+- [Thor] **Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers**, arXiv:2205.10893 [[paper](https://arxiv.org/abs/2205.10893)]
+- [Set] **Insights into pre-training via simpler synthetic tasks**, arXiv:2206.10139 [[paper](https://arxiv.org/abs/2206.10139)]
+- [PACT] **Proof artifact co-training for theorem proving with language models**, ICLR 2022 [[paper](https://arxiv.org/abs/2102.06203)]
+- :fire: [TaPEX] **TAPEX: Table Pre-training via Learning a Neural SQL Executor**, ICLR 2022 [[paper](https://arxiv.org/abs/2107.07653)]
+- :fire: [Minerva] **Solving quantitative reasoning problems with language models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2206.14858)]
+
+### Task-specific Fine-tuning for Math
+
+- [EPT] **Point to the expression: Solving algebraic word problems using the expression-pointer transformer model**, EMNLP 2020 [[paper](https://aclanthology.org/2020.emnlp-main.308/)]
+- [Generate \& Rank] **Generate \& Rank: A Multi-task Framework for Math Word Problems**, EMNLP 2021 [[paper](https://arxiv.org/abs/2109.03034)]
+- [RPKHS] **Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning**, EMNLP 2021 [[paper](https://aclanthology.org/2021.emnlp-main.272/)]
+- [PatchTRM] **IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning**, NeurIPS 2021 [[paper](https://arxiv.org/abs/2110.13214)]
+- :fire: [GSM8K-PLM] **Training verifiers to solve math word problems**, arXiv:2110.14168 [[paper](https://arxiv.org/abs/2110.14168)]
 - :fire: [Inter-GPS] **Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning**, ACL 2021 [[paper](https://aclanthology.org/2021.acl-long.528/)]
+- [Aristo] From ‘F’to ‘A’on the NY regents science exams: An overview of the aristo project, AI Magazine 2020 [paper]
+- [FinQANet] **FinQA: A Dataset of Numerical Reasoning over Financial Data**, EMNLP 2021 [[paper](https://arxiv.org/abs/2109.00122)]
+- [TAGOP] **TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**, ACL-JCNLP 2021 [[paper](https://arxiv.org/abs/2105.07624)]
+- [LAMT] **Linear algebra with transformers**, arXiv:2112.01898 [[paper](https://arxiv.org/abs/2112.01898)]
+- :fire: [Scratchpad] **Show your work: Scratchpads for intermediate computation with language models**, arXiv:2112.00114 [[paper](https://arxiv.org/abs/2112.00114)]
+- [Self-Sampling] **Learning from Self-Sampled Correct and Partially-Correct Programs**, arXiv:2205.14318 [[paper](https://arxiv.org/abs/2205.14318)]
+- [DeductReasoner] **Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction**, ACL 2022 [[paper](https://arxiv.org/abs/2203.10316)]
+- [DPE-NGS] **An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding**, COLING 2022 [[paper](https://aclanthology.org/2022.coling-1.130/)]
+- [BERT-TD+CL] **Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems**, Findings of ACL 2022 [[paper](https://arxiv.org/abs/2110.08464)]
+- [MT2Net] **MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.454/)]
+- [miniF2F-PLM] **MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics**, ICLR 2022 [[paper](https://arxiv.org/abs/2109.00110)]
+- :fire: [NaturalProver] **NaturalProver: Grounded Mathematical Proof Generation with Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.12910)]
 - :fire: [UniGeo] **UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression**, EMNLP 2022 [[paper](https://lupantech.github.io/papers/emnlp22_unigeo.pdf)]
+- :fire: [Bhaskara] **Lila: A Unified Benchmark for Mathematical Reasoning**, EMNLP 2022 [[paper](https://arxiv.org/abs/2210.17517)]
 
-### Language Models for Theorem Proving
 
-- **Generative Language Modeling for Automated Theorem Proving**, arXiv:2009.03393 [[paper](https://arxiv.org/abs/2009.03393)]
-- **HyperTree Proof Search for Neural Theorem Proving**, arXiv:2205.11491 [[paper](https://arxiv.org/abs/2205.11491)]
-- **Proof Artifact Co-training for Theorem Proving with Language Models**, ICLR 2022 [[paper](https://arxiv.org/abs/2102.06203)]
-- **Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.10893)]
-- [LISA] **LISA: Language models of ISAbelle proofs**, AITP 2021 [[paper](http://138.232.66.212/2021/abstract/paper_17.pdf)]
 
-### Language Models for MathQA
+## 🌠 In-context Learning for Mathematical Reasoning
 
-- **From 'F' to 'A' on the NY Regents Science Exams: An Overview of the Aristo Project**, arXiv:1909.01958 [[paper](https://arxiv.org/abs/1909.01958)]
-- **Injecting Numerical Reasoning Skills into Language Models**, ACL 2020 [[paper](https://arxiv.org/abs/2004.04487)]
-- **Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models**, arXiv:2112.06109 [[paper](https://arxiv.org/abs/2112.06109)]
+### General Large Language Models (100B+)
 
-### Language Models for Other Math Tasks
+- :fire: [GPT-3, 175B] **Language models are few-shot learners**, NeurIPS 2020 [[paper](https://arxiv.org/abs/2005.14165)]
+- :fire: [Codex, 175B] **Evaluating large language models trained on code**, arXiv:2107.03374 [[paper](https://arxiv.org/abs/2107.03374)]
+- :fire: [PaLM, 540B] **PaLM: Scaling Language Modeling with Pathways**, arXiv:2204.02311 [[paper](https://arxiv.org/abs/2204.02311)]
+- :fire: [ChatGPT, 175B] **ChatGPT: Optimizing Language Models for Dialogue**, November 30, 2022 [[website](https://openai.com/blog/chatgpt/)]
+- :question: [GPT-4]
 
-- **Linear algebra with transformers**, TMLR 2022 [[paper](https://arxiv.org/abs/2112.01898)]
-- **Show Your Work: Scratchpads for Intermediate Computation with Language Models**, arXiv:2112.00114 [[paper](https://arxiv.org/abs/2112.00114)] 
+### In-context Example Selection
 
+- :fire: [Few-shot-CoT] **Chain of thought prompting elicits reasoning in large language models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2201.11903)]
+- [Retrieval] **Learning to retrieve prompts for in-context learning**, NAACL-HLT 2022 [[paper](https://arxiv.org/abs/2112.08633)]
+- :fire: [PromptPG-CoT] **Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**, arXiv:2209.14610 [[paper](https://arxiv.org/abs/2209.14610)]
+- [Retrieval-CoT] **Automatic Chain of Thought Prompting in Large Language Models**, arXiv:2210.03493 [[paper](https://arxiv.org/abs/2210.03493)]
+- [Generate] **Generate rather than retrieve: Large language models are strong context generators**, arXiv:2209.10063 [[paper](https://arxiv.org/abs/2209.10063)]
+- [Complexity-CoT] **Complexity-Based Prompting for Multi-Step Reasoning,** arXiv:2210.00720 [[paper](https://arxiv.org/abs/2210.00720)]
+- [Auto-CoT] **Automatic Chain of Thought Prompting in Large Language Models**, arXiv:2210.03493 [[paper](https://arxiv.org/abs/2210.03493)]
 
+### High-quality Reasoning Chains
 
-## 🌠 In-context Learning with LLMs for Math
+- :fire: [Self-Consistency-CoT] **Self-consistency improves chain of thought reasoning in language models**, arXiv:2203.11171 [[paper](https://arxiv.org/abs/2203.11171)]
+- :fire: [Least-to-most CoT] **Least-to-Most Prompting Enables Complex Reasoning in Large Language Models**, arXiv:2205.10625 [[paper](https://arxiv.org/abs/2205.10625)]
+- **On the Advance of Making Language Models Better Reasoners**, arXiv:2206.02336 [[paper](https://arxiv.org/abs/2206.02336)]
+- **Decomposed prompting: A modular approach for solving complex tasks**, arXiv:2210.02406 [[paper](https://arxiv.org/abs/2210.02406)]
+- **PAL: Program-aided Language Models**, arXiv:2211.10435 [[paper](https://arxiv.org/abs/2211.10435)]
+- :fire: [Few-shot-PoT] **Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks**, arXiv:2211.12588 [[paper](https://arxiv.org/abs/2211.12588)]
 
-### Large Language Models (100B+)]
 
-- :fire: [GPT-3] **Language models are few-shot learners**, NeurIPS 2020 [[paper](https://arxiv.org/abs/2005.14165)]
-- :fire: [Codex] **Evaluating large language models trained on code**, arXiv:2107.03374 [[paper](https://arxiv.org/abs/2107.03374)]
-- :fire: [PaLM] **PaLM: Scaling Language Modeling with Pathways**, arXiv:2204.02311 [[paper](https://arxiv.org/abs/2204.02311)]
 
-### Prompt Learning for MWPs
+## ♣️ Other Related Work for Mathematical Reasoning
 
-- **Calibrate before use: Improving few-shot performance of language models**, ICML 2021 [[paper](https://arxiv.org/abs/2102.09690)]
-- **Emergent Abilities of Large Language Models**, Transactions on Machine Learning Research 2022 [[paper](https://arxiv.org/abs/2206.07682)]
-- **Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity**, ACL 2022 [[paper](https://arxiv.org/abs/2104.08786)]
-- **What Makes Good In-Context Examples for GPT-3?** The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures 2022 [[paper](https://aclanthology.org/2022.deelio-1.10/)]
-- :fire: [CoT] **Chain of thought prompting elicits reasoning in large language models**, arXiv:2201.11903 [[paper](https://arxiv.org/abs/2201.11903)]
-- :fire: **Self-consistency improves chain of thought reasoning in language models**, arXiv:2203.11171 [[paper](https://arxiv.org/abs/2203.11171)]
-- **Least-to-Most Prompting Enables Complex Reasoning in Large Language Models**, arXiv:2205.10625 [[paper](https://arxiv.org/abs/2205.10625)]
-- :fire: [Zero-shot CoT] **Large Language Models are Zero-Shot Reasoners**, preprint arXiv:2205.11916 [[paper](https://arxiv.org/abs/2205.11916)]
-- :fire: [CoT GPT-3 + RL] **Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**, arXiv:2209.14610, 2022 [[paper](https://arxiv.org/abs/2209.14610)]
-- :fire: **Language models are multilingual chain-of-thought reasoners**, arXiv:2210.03057 [[paper](https://arxiv.org/abs/2210.03057)]
-- **Automatic Chain of Thought Prompting in Large Language Models**, arXiv:2210.03493 [[paper](https://arxiv.org/abs/2210.03493)]
-- **Large Language Models are few(1)]-shot Table Reasoners**, arXiv:2210.06710 [[paper](https://arxiv.org/abs/2210.06710)]
-- **Challenging BIG-Bench tasks and whether chain-of-thought can solve them**, arXiv:2210.09261 [[paper](https://arxiv.org/abs/2210.09261)]
-- **Scaling Instruction-Finetuned Language Models**, arXiv:2210.11416 [[paper](https://arxiv.org/abs/2210.11416)]
+### Early Work
 
-### Prompt Learning for Proving
+- **Empirical explorations of the geometry theorem machine**, Western Joint IRE-AIEE-ACM Computer Conference 1960 [[paper](https://dl.acm.org/doi/10.1145/1460361.1460381)]
+- **Basic principles of mechanical theorem proving in elementary geometries**, Journal of Automated Reasoning 1986 [[paper](https://link.springer.com/article/10.1007/BF02328447)]
+- **Automated generation of readable proofs with geometric invariants**, Journal of Automated Reasoning 1996 [[paper](https://link.springer.com/article/10.1007/BF00283133)]
 
-- [PaLM, Codex] **Autoformalization with Large Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.12615)]
-- [Codex] **Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs**, arXiv:2210.12283 [[paper](https://arxiv.org/abs/2210.12283)]
+### Datasets
 
-### Prompt Learning for MathQA
+- :fire: [TextbookQA] **Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension**, CVPR 2017 [[paper](https://ieeexplore.ieee.org/document/8100054)]
+- :fire: [Raven] **Raven: A dataset for relational and analogical visual reasoning**, CVPR 2019 [[paper](https://arxiv.org/abs/1903.02741)]
+- [APPS] **Measuring Coding Challenge Competence With APPS**, NeurIPS 2021 [[paper](https://arxiv.org/abs/2105.09938)]
+- [PhysNLU] **PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics**, 2022 [[paper](https://arxiv.org/abs/2201.04275)]
 
-- :fire: **A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level**, PNAS 2022 [[paper](https://www.pnas.org/doi/10.1073/pnas.2123433119)]
-- :fire: **Minerva: Solving Quantitative Reasoning Problems with Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2206.14858)]
+### Methods
 
+- **My computer is an honor student—but how intelligent is it? Standardized tests as a measure of AI**, AI Magazine 2016 [[paper](https://ojs.aaai.org//index.php/aimagazine/article/view/2636)]
+- **Learning pipelines with limited data and domain knowledge: A study in parsing physics problems**, NeurIPS 2018 [[paper](https://proceedings.neurips.cc/paper/2018/hash/ac627ab1ccbdb62ec96e702f07f6425b-Abstract.html)]
+- **Automatically proving plane geometry theorems stated by text and diagram**, International Journal of Pattern Recognition and Artificial Intelligence 2019 [[paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001419400032)]
+- **Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language**, JCDL 2020 [[paper](https://arxiv.org/abs/2005.11021)]
 
+### Latest Work (To be classified)
 
-## ♣️ Other Methods for Math
+- :fire: **Advancing mathematics by guiding human intuition with AI**, Nature 2021 [[paper](https://www.nature.com/articles/s41586-021-04086-x)]
+- [MWPToolkit] **Mwptoolkit: an open-source framework for deep learning-based math word problem solvers**, AAAI 2022 [[paper](https://arxiv.org/abs/2109.00799)]
+- **A deep reinforcement learning agent for geometry online tutoring**, Knowledge and Information Systems 2022 [[paper](https://link.springer.com/article/10.1007/s10115-022-01804-3)]
+- **ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2210.10105)]
+- **Solving math word problems with processand outcome-based feedback**, arXiv:2211.14275 [[paper](https://arxiv.org/abs/2211.14275)]
+- **APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning**, arXiv:2212.07249 [[paper](https://arxiv.org/abs/2212.07249)]
+- **Enhancing Financial Table and Text Question Answering with Tabular Graph and Numerical Reasoning**, AACL 2022 [[paper](https://aclanthology.org/2022.aacl-main.72/)]
+- **DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data**, AAAI 2023 [[paper](https://arxiv.org/abs/2211.12668)]
+- **Generalizing Math Word Problem Solvers via Solution Diversification**, arXiv:2212.00833 [[paper](https://arxiv.org/abs/2212.00833)]
+- **Textual Enhanced Contrastive Learning for Solving Math Word Problems**, arXiv:2211.16022 [[paper](https://arxiv.org/abs/2211.16022)]
+- **Analogical Math Word Problems Solving with Enhanced Problem-Solution Association**, EMNLP 2022 [[paper](https://arxiv.org/abs/2212.00837)]
 
-### Early Methods
 
-- **Empirical explorations of the geometry theorem machine**, Western Joint IRE-AIEE-ACM Computer Conference 1960 [[paper](https://dl.acm.org/doi/10.1145/1460361.1460381)]
-- **Basic principles of mechanical theorem proving in elementary geometries**, Journal of Automated Reasoning 1986 [[paper](https://link.springer.com/article/10.1007/BF02328447)]
-- **Automated generation of readable proofs with geometric invariants**, Journal of Automated Reasoning 1996 [[paper](https://link.springer.com/article/10.1007/BF00283133)]
-- **My computer is an honor student—but how intelligent is it? Standardized tests as a measure of AI**, AI Magazine 2016 [[paper](https://ojs.aaai.org//index.php/aimagazine/article/view/2636)]
 
-### Symbolic Methods
+## Citation
 
-- **Learning pipelines with limited data and domain knowledge: A study in parsing physics problems**, NeurIPS 2018 [[paper](https://proceedings.neurips.cc/paper/2018/hash/ac627ab1ccbdb62ec96e702f07f6425b-Abstract.html)]
-- **Automatically proving plane geometry theorems stated by text and diagram**, International Journal of Pattern Recognition and Artificial Intelligence 2019 [[paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001419400032)]
+If you find this repo useful, please kindly cite our survey:
 
-### Pure ML Methods
+```
+@article{lu2022dl4math,
+  title={A Survey of Deep Learning for Mathematical Reasoning},
+  author={Lu, Pan and Qiu, Liang and Yu, Wenhao and Welleck, Sean and Chang, Kai-Wei},
+  journal={arXiv preprint arXiv:2212.10535},
+  year={2022}
+}
+```
 
-- **Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language**, JCDL 2020 [[paper](https://arxiv.org/abs/2005.11021)]
diff --git a/history/.DS_Store b/history/.DS_Store
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diff --git a/history/README-v1.md b/history/README-v1.md
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-# DL4MATH - Reading List
-
-
-
-## 🧰 Resources
-
-### Related Survey
-
-- **Representing Numbers in NLP: a Survey and a Vision**, NACL 2021 [[paper]](https://aclanthology.org/2021.naacl-main.53/)
-- **Partial Differential Equations Meet Deep Neural Networks**: A Survey, arXiv:2211.05567 [[paper]](https://arxiv.org/abs/2211.05567)
-- **A Survey in Mathematical Language Processing**, arXiv:2205.15231 [[paper](https://arxiv.org/abs/2205.15231)]
-- **The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers**, TPAMI 2019 [[paper]](https://arxiv.org/abs/1808.07290)
-- **A Survey of Question Answering for Math and Science Problem**, arXiv:1705.04530 [[paper]](https://arxiv.org/abs/1705.04530)
-
-### Workshops
-
-- :fire: **The 2nd MATH-AI Workshop: Toward Human-Level Mathematical Reasoning**, NeurIPS 2022  [[website]](https://mathai2022.github.io/)
-- **The 1st Workshop on Mathematical Natural Language Processing**, EMNLP 2022 [[website]](https://sites.google.com/view/1st-mathnlp/)
-- **Math AI for Education: Bridging the Gap Between Research and Smart Education (MATHAI4ED)**, NeurIPS 2021 [[website]](https://mathai4ed.github.io/)
-- **The 1st MATH-AI Workshop: the Role of Mathematical Reasoning in General Artificial Intelligence**, ICLR 2021 [[website]](https://mathai-iclr.github.io/)
-
-### Talks
-
-- **Computer Scientist Explains One Concept in 5 Levels of Difficulty**, 2022 [[YouTube]](https://www.youtube.com/watch?v=fOGdb1CTu5c)
-
-
-
-## 🌠 Large Language Models
-
-- :fire: [PaLM] **PaLM: Scaling Language Modeling with Pathways**, arXiv:2204.02311 [[paper](https://arxiv.org/abs/2204.02311)]
-- :fire: [Codex] **Evaluating large language models trained on code**, arXiv:2107.03374 [[paper](https://arxiv.org/abs/2107.03374)]
-- :fire: [GPT-3] **Language models are few-shot learners**, NeurIPS 2020 [[paper](https://arxiv.org/abs/2005.14165)]
-- [UnifiedQA] **UNIFIEDQA: Crossing Format Boundaries with a Single QA System**, EMNLP 2020 [[paper](https://arxiv.org/abs/2005.00700)]
-- [GPT-2] **Language models are unsupervised multitask learners**, 2019 [[paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)]
-
-
-
-## 🧮 Math Word Problems (MWPs)
-
-### Neural Networks for MWPs
-
-- 🎨 [ArMATH] **ArMATH: a Dataset for Solving Arabic Math Word Problems**, LREC 2022 [[paper](https://aclanthology.org/2022.lrec-1.37/)]
-- **Analogical Math Word Problems Solving with Enhanced Problem-Solution Association**, EMNLP 2022 [[paper](https://aclanthology.org/2021.emnlp-main.272/)]
-- 🎨 [SVAMP] **Are NLP Models really able to Solve Simple Math Word Problems?**, NAACL-HIT 2021 [[paper](https://arxiv.org/abs/2103.07191)]
-- **Solving Math Word Problems with Teacher Supervision**, IJCAI 2021 [[paper](https://www.ijcai.org/proceedings/2021/485)]
-- [Weak Supervision] **Learning by Fixing: Solving Math Word Problems with Weak Supervision**, AAAI 2021 [[paper](https://arxiv.org/abs/2012.10582)]
-- [expression tree] **Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems**, EMNLP 2020 [[paper](https://arxiv.org/abs/2010.06823)]
-- 🎨 [ASDiv-A] **A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers**, ACL 2020 [[paper](https://arxiv.org/abs/2106.15772)]
-- [equation templates] **Template-based math word problem solvers with recursive neural networks**, AAAI 2019 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/4697)]
-- 🎨 [MathQA] **MathQA: Towards interpretable math word problem solving with operation-based formalisms**, NAACL-HLT 2019 [[paper](https://aclanthology.org/N19-1245/)]
-- [expression tree] **Translating a math word problem to an expression tree**, EMNLP 2018 [[paper](https://aclanthology.org/D18-1132/)]
-- [logical reasoning] **Mapping to declarative knowledge for word problem solving**, TACL 2018 [[paper](https://arxiv.org/abs/1712.09391)]
-- [symbolic reasoning] **Semantic parsing of pre-university math problems**, ACL 2017 [[paper](https://aclanthology.org/P17-1195/)]
-- [Equation templates] **Learning fine-grained expressions to solve math word problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1084/)]
-- [Math23K] **Deep neural solver for math word problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1088/)]
-- [Dependency Graph] **Unit dependency graph and its application to arithmetic word problem solving**, AAAI 2017 [[paper](https://arxiv.org/abs/1612.00969)]
-- 🎨 [DRAW-1K] **Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems**, ACL 2017 [[paper](https://aclanthology.org/E17-1047/)]
-- **Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems**, ACL 2017 [[paper](https://aclanthology.org/P17-1015/)]
-- 🎨 [Dolphin18K] **How well do computers solve math word problems? large-scale dataset construction and evaluation**, ACL 2016 [[paper](https://aclanthology.org/P16-1084/)]
-- 🎨 [MAWPS] **MAWPS: A math word problem repository**, NAACL-HLT 2016 [[paper](https://aclanthology.org/N16-1136/)]
-
-### Larguage Models for MWPs
-
-- **Solving quantitative reasoning problems with language models**, arXiv:2206.14858 [[paper](https://arxiv.org/abs/2206.14858)]
-- **Learning from Self-Sampled Correct and Partially-Correct Programs**, arXiv:2205.14318 [[paper]](https://arxiv.org/abs/2205.14318)
-- **Minerva: Solving Quantitative Reasoning Problems with Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2206.14858)]
-- **Insights into Pre-training via Simpler Synthetic Tasks**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2206.10139)]
-- **TAPEX: Table Pre-training via Learning a Neural SQL Executor**, ICLR 2022 [[paper](https://arxiv.org/abs/2107.07653)]
-- 🎨 [MultiHiertt] **MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.454/)]
-- **MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving**, Findings of NAACL 2022 [[paper](https://arxiv.org/abs/2107.13435)]
-- 🎨 [GSM8K] **Training verifiers to solve math word problems**, arXiv:2110.14168 [[paper](https://arxiv.org/abs/2110.14168)]
-- **Lime: Learning inductive bias for primitives of mathematical reasoning**, ICML 2021 [[paper](https://arxiv.org/abs/2101.06223)]
-- 🎨 [IconQA] **IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning**, NeurIPS 2021 (Datasets and Benchmarks) [[paper]](https://arxiv.org/abs/2110.13214)
-- 🎨 [TAT-QA] **TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**, ACL-JCNLP 2021 [[paper](https://arxiv.org/abs/2105.07624)]
-- 🎨 [FinQA] **FinQA: A Dataset of Numerical Reasoning over Financial Data**, EMNLP 2021 [[paper](https://arxiv.org/abs/2109.00122)]
-
-### Prompting Learning for MWPs
-
-- **Scaling Instruction-Finetuned Language Models**, arXiv:2210.11416 [[paper](https://arxiv.org/abs/2210.11416)]
-- **Challenging BIG-Bench tasks and whether chain-of-thought can solve them**, arXiv:2210.09261 [[paper](https://arxiv.org/abs/2210.09261)]
-- **Large Language Models are few(1)-shot Table Reasoners**, arXiv:2210.06710 [[paper](https://arxiv.org/abs/2210.06710)]
-- **Automatic Chain of Thought Prompting in Large Language Models**, arXiv:2210.03493 [[paper]](https://arxiv.org/abs/2210.03493)
-- :fire: **Language models are multilingual chain-of-thought reasoners**, arXiv:2210.03057 [[paper](https://arxiv.org/abs/2210.03057)]
-- :fire: 🎨 [TabMWP, GPT-3] **Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**, arXiv:2209.14610, 2022 [[paper]](https://arxiv.org/abs/2209.14610)
-- :fire: [Zero-shot CoT] **Large Language Models are Zero-Shot Reasoners**, preprint arXiv:2205.11916 [[paper](https://arxiv.org/abs/2205.11916)]
-- **Least-to-Most Prompting Enables Complex Reasoning in Large Language Models**, arXiv:2205.10625 [[paper](https://arxiv.org/abs/2205.10625)]
-- :fire: **Self-consistency improves chain of thought reasoning in language models**, arXiv:2203.11171 [[paper](https://arxiv.org/abs/2203.11171)]
-- :fire: [CoT] **Chain of thought prompting elicits reasoning in large language models**, arXiv:2201.11903 [[paper](https://arxiv.org/abs/2201.11903)]
-- **Emergent Abilities of Large Language Models**, Transactions on Machine Learning Research 2022 [[paper](https://arxiv.org/abs/2206.07682)]
-- **Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity**, ACL 2022 [[paper](https://arxiv.org/abs/2104.08786)]
-- 🎨 [NumGLUE] **NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks**, ACL 2022 [[paper](https://aclanthology.org/2022.acl-long.246/)]
-- **What Makes Good In-Context Examples for GPT-3?** The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures 2022 [[paper](https://aclanthology.org/2022.deelio-1.10/)]
-- **Calibrate before use: Improving few-shot performance of language models**, ICML 2021 [[paper](https://arxiv.org/abs/2102.09690)]
-
-
-
-## 📜 Theorem Proving
-
-### Formal Theorem Proving
-
-- **Autoformalization with Large Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.12615)]
-- **HyperTree Proof Search for Neural Theorem Proving**, arXiv:2205.11491 [[paper]](https://arxiv.org/abs/2205.11491)
-- **Autoformalization with Large Language Models**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.12615)]
-- **Proof Artifact Co-training for Theorem Proving with Language Models**, arXiv:2102.06203 [[paper]](https://arxiv.org/abs/2102.06203)
-- **INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving**, ICLR 2021 [[paper]](https://arxiv.org/abs/2007.02924)
-- **Graph representations for higher-order logic and theorem proving**, AAAI 2020 [[paper](https://ojs.aaai.org//index.php/AAAI/article/view/5689)]
-- **Generative Language Modeling for Automated Theorem Proving**, arXiv:2009.03393 [[paper]](https://arxiv.org/abs/2009.03393)
-- **Deep network guided proof search**, arXiv:1701.06972 [[paper](https://arxiv.org/abs/1701.06972)]
-- **DeepMath - Deep Sequence Models for Premise Selection**, NeurIPS 2016 [[paper](https://arxiv.org/abs/1606.04442)]
-- **Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers**, NeurIPS 2022 [[paper](https://arxiv.org/abs/2205.10893)]
-- **Proof Artifact Co-training for Theorem Proving with Language Model**, ICLR 2022 [[paper](https://arxiv.org/abs/2102.06203)]
-- **Learning to Give Checkable Answers with Prover-Verifier Games**, arXiv:2108.12099 [[paper](https://arxiv.org/abs/2108.12099)]
-- **IsarStep: a Benchmark for High-level Mathematical Reasoning**, ICLR 2021 [[paper](https://arxiv.org/abs/2006.09265)]
-- **REFACTOR: Learning to Extract Theorems from Proofs**, 2022 [[paper](https://openreview.net/forum?id=827jG3ahxL)]
-- **Latent Action Space for Efficient Planning in Theorem Proving**, 2021 [[paper](http://aitp-conference.org/2021/abstract/paper_24.pdf)]
-- **Neural Theorem Proving on Inequality Problems**, AITP 2020 [[paper](http://aitp-conference.org/2020/abstract/paper_18.pdf)]
-
-### Informal Theorem Proving
-
-- **Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs**, arXiv:2210.12283 [[paper](https://arxiv.org/abs/2210.12283)]
-- :fire: 🎨 **NaturalProofs: Mathematical Theorem Proving in Natural Language**, NeurIPS 2021 (Datasets and Benchmarks) [[paper]](https://arxiv.org/abs/2104.01112)
-
-### Geometry Theorem Proving
-
-- **A Framework for Solving Explicit Arithmetic Word Problems and Proving Plane Geometry Theorems**, International Journal of Pattern Recognition and Artificial Intelligence 2019 [[paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001419400056)]
-- **Automatically proving plane geometry theorems stated by text and diagram**, International Journal of Pattern Recognition and Artificial Intelligence 2019 [[paper](https://www.worldscientific.com/doi/abs/10.1142/S0218001419400032)]
-- **Automated generation of readable proofs with geometric invariants**, Journal of Automated Reasoning 1996 [[paper](https://link.springer.com/article/10.1007/BF00283133)]
-- **Basic principles of mechanical theorem proving in elementary geometries**, Journal of Automated Reasoning 1986 [[paper](https://link.springer.com/article/10.1007/BF02328447)]
-- **Empirical explorations of the geometry theorem machine**, Western Joint IRE-AIEE-ACM Computer Conference 1960 [[paper](https://dl.acm.org/doi/10.1145/1460361.1460381)]
-
-
-
-## ♣️ Other Math Problems
-
-### 📐Geometry Problem Solving
-
-- :fire: **UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression**, EMNLP 2022 [[paper]](https://lupantech.github.io/papers/emnlp22_unigeo.pdf)
-- :fire: 🎨 [Geometry3K, Neuro-symblic] **Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning**, ACL 2021 [[paper]](https://aclanthology.org/2021.acl-long.528/)
-- 🎨 [GeoQA] **GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning**, Findings of ACL 2021 [[paper]](https://aclanthology.org/2021.findings-acl.46.pdf)
-- [Knowledge] **Discourse in multimedia: A case study in extracting geometry knowledge from textbooks**, Computational Linguistics, 2020 [[paper](https://aclanthology.org/J19-4002/)]
-- **Automatic understanding and formalization of natural language geometry problems using syntax-semantics models**, International Journal of Innovative Computing, Information and Control 2018 [[paper](http://www.ijicic.org/ijicic-140106.pdf)]
-- 🎨 [GEOS++] **From textbooks to knowledge: A case study in harvesting axiomatic knowledge from textbooks to solve geometry problems**, EMNLP 2017 [[paper](https://aclanthology.org/D17-1081/)]
-- 🎨 [GeoShader] **Synthesis of solutions for shaded area geometry problems**, The Thirtieth International Flairs Conference, 2017 [[paper](https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/viewFile/15416/14902)]
-- 🎨 [GEOS-OS] **Learning to solve geometry problems from natural language demonstrations in textbooks**, Proceedings of the 6th Joint Conference on Lexical and Computational Semantics, 2017 [[paper](https://aclanthology.org/S17-1029/)]
-- **Retrieving geometric information from images: the case of hand-drawn diagrams**, Data Mining and Knowledge Discovery 2017 [[paper](https://link.springer.com/article/10.1007/s10618-017-0494-1)]
-- 🎨 [GEOS] **Solving geometry problems: Combining text and diagram interpretation**, EMNLP 2015 [[paper](https://aclanthology.org/D15-1171/)]
-
-- **Synthesis of geometry proof problems**, AAAI 2014 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/8745)]
-- **Diagram understanding in geometry questions**, AAAI 2014 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/9146#:~:text=The%20first%20step%20in%20solving,them%20to%20corresponding%20textual%20descriptions.)]
-
-### 🏛️ Advanced Mathematics
-
-- :fire: 🎨 **Lila: A Unified Benchmark for Mathematical Reasoning**, EMNLP 2022 [[paper]](https://arxiv.org/abs/2210.17517)
-- :fire: **A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level**, PNAS 2022 [[paper]](https://www.pnas.org/doi/10.1073/pnas.2123433119)
-- **Linear algebra with transformers**, TMLR 2022 [[paper]](https://arxiv.org/abs/2112.01898)
-- :fire: **Advancing mathematics by guiding human intuition with AI**, Nature 2021 [[paper]](https://www.nature.com/articles/s41586-021-04086-x)
-
-### 🏔️ Science Problems
-
-- 🎨 **Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering**, NeurIPS 2022 [[paper]](https://arxiv.org/abs/2209.09513)
-- **From 'F' to 'A' on the NY Regents Science Exams: An Overview of the Aristo Project**, arXiv:1909.01958 [[paper](https://arxiv.org/abs/1909.01958)]
-- 🎨 **Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension**, CVPR 2017 [[paper]](https://ieeexplore.ieee.org/document/8100054)
-- 🎨 **PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics** [[paper]](https://arxiv.org/abs/2201.04275)
-- **My computer is an honor student—but how intelligent is it? Standardized tests as a measure of AI**, AI Magazine 2016 [[paper](https://ojs.aaai.org//index.php/aimagazine/article/view/2636)]
--  **Combining retrieval, statistics, and inference to answer elementary science questions**, AAAI 2016 [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/10325)] 
-
-### Diagram Problems
-
-- **Show Your Work: Scratchpads for Intermediate Computation with Language Models**, arXiv:2112.00114 [[paper](https://arxiv.org/abs/2112.00114)] 
-- 🎨 [Raven] **Raven: A dataset for relational and analogical visual reasoning**, CVPR 2019 [[paper](https://arxiv.org/abs/1903.02741)]
-- 🎨 [Dvqa] **Dvqa: Understanding data visualizations via question answering**, CVPR 2018 [[paper](https://arxiv.org/abs/1801.08163)]
-- **Learning pipelines with limited data and domain knowledge: A study in parsing physics problems**, NeurIPS 2018 [[paper](https://proceedings.neurips.cc/paper/2018/hash/ac627ab1ccbdb62ec96e702f07f6425b-Abstract.html)]
-- 🎨 [NLVR] **A corpus of natural language for visual reasoning**, ACL 2017 [[paper](https://aclanthology.org/P17-2034/)]
-- 🎨 [Figureqa] **Figureqa: An annotated figure dataset for visual reasoning**, arXiv:1710.07300 [[paper](https://arxiv.org/abs/1710.07300)]
-- 🎨 [AI2D] **A Diagram is Worth a Dozen Images**, ECCV 2016 [[paper](https://arxiv.org/abs/1603.07396)]
-- **Semantic parsing to probabilistic programs for situated question answering**, EMNLP 2016 [[paper](https://aclanthology.org/D16-1016/)]
-
-### Other Mathematical Problems
-
-- **Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics**, AAAI 2022 [[paper](https://arxiv.org/abs/2109.13986)]
-- **Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models**, arXiv:2112.06109 [[paper](https://arxiv.org/abs/2112.06109)]
-- 🎨 **Programming Puzzles**, NeurIPS 2021 (Datasets and Benchmarks) [[paper](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/3988c7f88ebcb58c6ce932b957b6f332-Abstract-round1.html)]
-- 🎨 [Fermi] **How Much Coffee Was Consumed During EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI**, EMNLP 2020 [[paper]](https://arxiv.org/abs/2110.14207)
-- **Injecting Numerical Reasoning Skills into Language Models**, ACL 2020 [[paper](https://arxiv.org/abs/2004.04487)]
-- 🎨 **Machine Number Sense: A Dataset of Visual Arithmetic Problems for Abstract and Relational Reasoning**, AAAI 2020 [[paper](https://arxiv.org/abs/2004.12193)]
-- **Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language**, JCDL 2020 [[paper](https://arxiv.org/abs/2005.11021)]
-
diff --git a/history/backup.md b/history/backup.md
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@@ -1,10 +0,0 @@
-# DL4MATH-Reading
-
-
-
-
-
-
-
-
-