Large Language Model System Benchmark

A benchmark Paper of data science code generation LLM System

ICASSP Data Science Code Dataset

Dataset Structure Example:

• Approaches differ from each other due to different ways to split the code/ divide the task.
• Parts differ from each other due to different part after split.

├── Paper1

		├── Approach 1

		├── Approach 2

		├──Approach 3

      			├── Part 1

      			├── Part 2

           			├── ans

           			├── input

           			├── .cfg

           			├── split.txt

       				├── details.txt

           			├── prompt.txt

           			├── code_context.txt

           			├── test_code.py         (compare the output)

           			├──test_generate_pickle.py (run the generated code)

├── Paper2

├── Paper3

├── Paper4

High Level Approach with minor split information

• .cfg contains the meta info of the paper, including the background, high-level idea of the whole paper.
• split.txt Requires the system to split and finish the high-level task by itself without providing additional tips.
• details.txt Blank in this approach case.
• prompt.txt is the official prompt we recommend using for querying large language models. Prompt.txt will include information from .cfg and split , code_context.txt, and also some comments/ key points extracted from the code(details.txt).
• ans and input contain the pickles file caching the input and solution objects.
• code_context.txt is the executable code context for evaluation.
• test_code.py contains the testing code and the ground truth solution code.
• test_generate_pickle.py is the script we use the generate the input pickle files in input

If the dataset is ever corrupted, you can run python cache_input_ans.py to regenerate input and ans in each problem directory.

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Several approaches between them

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Low Level Approach with minor split

• .cfg contains the meta info of the paper, including the background, high-level idea of the whole paper
• split.txt contains the information of all parts of the code and . (Read Data, Data Preprocessing, Reconstruct Dataset Class, Data Cleaning, Models, Training, Loss Function, Evaluation, Metrics )
• details.txt contains the comments/ key points extracted from the ground truth solution code.
• prompt.txt is the official prompt we recommend using for querying large language models. Prompt.txt will include information from **.cfg, details ,context.txt**and split
• ans and input contain the pickles file caching the input and solution objects.
• code_context.txt is the executable code context for evaluation.
• test_code.py contains the testing code and the ground truth solution code.
• test_generate_pickle.py is the script we use the generate the input pickle files in input

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