Update readme.

README.md

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 LLMLingua-2 is a branch of work from project:
-# [LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression](https://llmlingua.com/)
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-        <h2 align="center">LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression</h2>
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-<!-- <p align="center">
-    | <a href="https://llmlingua.com/"><b>Project Page</b></a> |
-    <a href="https://aclanthology.org/2023.emnlp-main.825/"><b>LLMLingua</b></a> |
-    <a href="https://arxiv.org/abs/2310.06839"><b>LongLLMLingua</b></a> |
-    <a href="https://arxiv.org/abs/2403."><b>LLMLingua-2</b></a> |
-    <a href="https://huggingface.co/spaces/microsoft/LLMLingua"><b>LLMLingua Demo</b></a> |
-    <a href="https://huggingface.co/spaces/microsoft/LLMLingua-2"><b>LLMLingua-2 Demo</b></a> |
-</p> -->
+
+# LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression
 | [**Project Page**](https://llmlingua.com/) | [**LLMLingua**](https://aclanthology.org/2023.emnlp-main.825/) | [**LongLLMLingua**](https://arxiv.org/abs/2310.06839) | [**LLMLingua-2**](https://arxiv.org/abs/2403.12968) | [**LLMLingua Demo**](https://huggingface.co/spaces/microsoft/LLMLingua) | [**LLMLingua-2 Demo**](https://huggingface.co/spaces/microsoft/LLMLingua-2) |
 
-Check the links above for more information.
-## Brief Introduction
+Check the links above for more information!
+
+## Brief Introduction 📚
  
 **LLMLingua** utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.
- 
 - [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://aclanthology.org/2023.emnlp-main.825/) (EMNLP 2023)<br>
   _Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu_
  
 **LongLLMLingua** mitigates the 'lost in the middle' issue in LLMs, enhancing long-context information processing. It reduces costs and boosts efficiency with prompt compression, improving RAG performance by up to 21.4% using only 1/4 of the tokens.
- 
 - [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://arxiv.org/abs/2310.06839) (ICLR ME-FoMo 2024)<br>
   _Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu_
  
 **LLMLingua-2**, a small-size yet powerful prompt compression method trained via data distillation from GPT-4 for token classification with a BERT-level encoder, excels in task-agnostic compression. It surpasses LLMLingua in handling out-of-domain data, offering 3x-6x faster performance.
- 
 - [LLMLingua-2: Context-Aware Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://arxiv.org/abs/2403.) (Under Review)<br>
   _Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Ruhle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang_