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-# Large Multimoal Models Evaluation Suite
+# The Evaluation Suite of Large Multimodal Models 
 
-> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
+> Accelerating the development of large multimodal models (LMMs) with `lmms-eval`
 
 🏠 [Homepage](https://lmms-lab.github.io/) |  🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
 
 In an era where people pursue AGI (Artificial General Intelligence) with the zeal akin to 1960s moon landing mission. 
-Evaluating the core of AGI, the large-scale language models (LLMs) and multi-modality models (LMMs) with unprecedented capabilities, has become a pivotal challenge. These foundation models are at the heart of AGI's development, representing critical milestones in our quest to achieve intelligent systems that can understand, learn, and interact across a broad range of human tasks. 
+Evaluating the core of AGI, the large language models (LLMs) and large multimodal models (LMMs) with unprecedented capabilities, has become a pivotal challenge. These foundation models are at the heart of AGI's development, representing critical milestones in our quest to achieve intelligent systems that can understand, learn, and interact across a broad range of human tasks. 
 
 To surmount this, a broad spectrum of datasets is proposed and used to assess model capabilities across various dimensions, creating a comprehensive capability chart that reveals the true performance of models. However, evaluation of models has become quite hard since there are countless evaluation benchmarks and datasets organized in various ways, scattered across the internet, sleeping in somebody's Google Drive, Dropbox, and other websites hosted by schools or research labs.
 
 In the field of language models, there has been a valuable precedent set by the work of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). They offer integrated data and model interfaces, enabling rapid evaluation of language models and serving as the backend support framework for the [open-llm-leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and has gradually become the underlying ecosystem of the era of foundation models.
 
-However, the evaluation of multi-modality models is still in its infancy, and there is no unified evaluation framework that can be used to evaluate multi-modality models across a wide range of datasets. To address this challenge, we introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of Large Multimoal Models (LMMs).
+However, though there are many new evaluation datasets are recently proposed, the efficient evaluation pipeline of LMM is still in its infancy, and there is no unified evaluation framework that can be used to evaluate LMM across a wide range of datasets. To address this challenge, we introduce **lmms-eval**, an evaluation framework meticulously crafted for consistent and efficient evaluation of LMM.
 
 We humbly obsorbed the exquisite and efficient design of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). Building upon its foundation, we implemented our `lmms-eval` framework with performance optimizations specifically for LMMs.
 
 ## Necessity of lmms-eval
 
-We believe our effort could provide an efficient interface for the detailed comparison of publicly available models to discern their strengths and weaknesses. It's also useful for research institutions and production-oriented companies to accelerate the development of large-scale multi-modality models. With the `lmms-eval`, we have significantly accelerated the lifecycle of model iteration. Inside the LLaVA team, the utilization of `lmms-eval` largely improves the efficiency of the model development cycle, as we are able to evaluate weekly trained hundreds of checkpoints on 20-30 datasets, identifying the strengths and weaknesses, and then make targeted improvements.
+We believe our effort could provide an efficient interface for the detailed comparison of publicly available models to discern their strengths and weaknesses. It's also useful for research institutions and production-oriented companies to accelerate the development of large multimodal models. With the `lmms-eval`, we have significantly accelerated the lifecycle of model iteration. Inside the LLaVA team, the utilization of `lmms-eval` largely improves the efficiency of the model development cycle, as we are able to evaluate weekly trained hundreds of checkpoints on 20-30 datasets, identifying the strengths and weaknesses, and then make targeted improvements.
 
 # Annoucement