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README.md

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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/zdkwKUqrPy)
 
 
-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.
+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.
 
 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.