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Feature(LLMLingua): update the news (#9)
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<p align="center">
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| <a href="https://arxiv.org/abs/2310.05736"><b>LLMLingua Paper</b></a> | <a href="https://arxiv.org/abs/2310.06839"><b>LongLLMLingua Paper</b></a> | <a href="https://huggingface.co/spaces/microsoft/LLMLingua"><b>HF Space Demo</b></a> |
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| <a href="https://llmlingua.com/"><b>Project Page</b></a>| <a href="https://arxiv.org/abs/2310.05736"><b>LLMLingua Paper</b></a> | <a href="https://arxiv.org/abs/2310.06839"><b>LongLLMLingua Paper</b></a> | <a href="https://huggingface.co/spaces/microsoft/LLMLingua"><b>HF Space Demo</b></a> |
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</p>
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https://github.com/microsoft/LLMLingua/assets/30883354/eb0ea70d-6d4c-4aa7-8977-61f94bb87438
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## News
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- 🎈 We launched a [project page](https://llmlingua.com/) showcasing real-world case studies, including RAG, Online Meetings, CoT, and Code;
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- 👾 LongLLMLingua has been incorporated into the [LlamaIndex pipeline](https://github.com/run-llama/llama_index/blob/main/llama_index/indices/postprocessor/longllmlingua.py), which is a widely used RAG framework.
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## Tl;DR
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LLMLingua, that uses a well-trained small language model after alignment, such as GPT2-small or LLaMA-7B, to detect the unimportant tokens in the prompt and enable inference with the compressed prompt in black-box LLMs, achieving up to 20x compression with minimal performance loss.
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