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 🏠 [Homepage](https://lmms-lab.github.io/) |  🎉 [Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) | <a href="https://emoji.gg/emoji/1684-discord-thread"><img src="https://cdn3.emoji.gg/emojis/1684-discord-thread.png" width="14px" height="14px" alt="Discord_Thread"></a> [discord/lmms-eval](https://discord.gg/ebAMGSsS)
 
 
-In today's world, we're on a thrilling quest for Artificial General Intelligence (AGI), driven by a passion that reminds us of the excitement surrounding the 1960s moon landing. At the heart of this adventure are the incredible large language models (LLMs) and large multimodal models (LMMs). These models are like brilliant minds that can understand, learn, and interact with a vast array of human tasks, marking a significant leap toward our goal.
+In today's world, we're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks. These advancements bring us closer to achieving AGI.
 
-To truly understand how capable these models are, we've started to create and use a wide variety of evaluation benchmarks. These benchmarks help us map out a detailed chart of abilities, showing us how close we are to achieving true AGI. However, this journey is not without its challenges. The sheer number of benchmarks and datasets we need to look at is overwhelming. They're all over the place - tucked away in someone's Google Drive, scattered across Dropbox, and hidden in the corners of various school and research lab websites. It's like embarking on a treasure hunt where the maps are spread far and wide.
+To gauge how advanced these models are, we use a variety of evaluation benchmarks. These benchmarks are tools that help us understand the capabilities of these models, showing us how close we are to achieving AGI. However, finding and using these benchmarks is a big challenge. The necessary benchmarks and datasets are spread out and hidden in various places like Google Drive, Dropbox, and different school and research lab websites. It feels like we're on a treasure hunt, but the maps are scattered everywhere.
 
 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.