Feature(LLMLingua): update information

README.md

@@ -13,27 +13,47 @@ license: mit
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
-<div style="display: flex; align-items: center; ">  
-    <div style="width: 100px; margin-right: 10px; height:auto;" align="left">  
-        <img src="images/LLMLingua_logo.png" alt="LLMLingua" width="100" align="left">  
-    </div>  
-    <div style="flex-grow: 1;" align="center">  
-        <h2 align="center">LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models & LongLLMLingua</h1>  
-    </div>  
+<div style="display: flex; align-items: center;">
+    <div style="width: 100px; margin-right: 10px; height:auto;" align="left">
+        <img src="images/LLMLingua_logo.png" alt="LLMLingua" width="100" align="left">
+    </div>
+    <div style="flex-grow: 1;" align="center">
+        <h2 align="center">LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression</h2>
+    </div>
 </div>
 
 <p align="center">
-| <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> |
+    | <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>
 
-## Tl;DR
+## News
 
-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.
+- 🦚 We're excited to announce the release of **LLMLingua-2**, boasting a 3x-6x speed improvement over LLMLingua! For more information, check out our [paper](https://arxiv.org/abs/2403.), visit the [project page](https://llmlingua.com/llmlingua-2.html), and explore our [demo](https://huggingface.co/spaces/microsoft/LLMLingua-2).
+- 🤳 Talk slides are available in [AI Time Jan, 24](https://drive.google.com/file/d/1fzK3wOvy2boF7XzaYuq2bQ3jFeP1WMk3/view?usp=sharing).
+- 🖥 EMNLP'23 slides are available in [Session 5](https://drive.google.com/file/d/1GxQLAEN8bBB2yiEdQdW4UKoJzZc0es9t/view) and [BoF-6](https://drive.google.com/file/d/1LJBUfJrKxbpdkwo13SgPOqugk-UjLVIF/view).
+- 📚 Check out our new [blog post](https://medium.com/@iofu728/longllmlingua-bye-bye-to-middle-loss-and-save-on-your-rag-costs-via-prompt-compression-54b559b9ddf7) discussing RAG benefits and cost savings through prompt compression. See the script example [here](https://github.com/microsoft/LLMLingua/blob/main/examples/Retrieval.ipynb).
+- 🎈 Visit our [project page](https://llmlingua.com/) for real-world case studies in RAG, Online Meetings, CoT, and Code.
+- 👨‍🦯 Explore our ['./examples'](https://github.com/microsoft/LLMLingua/blob/main/examples) directory for practical applications, including [RAG](https://github.com/microsoft/LLMLingua/blob/main/examples/RAG.ipynb), [Online Meeting](https://github.com/microsoft/LLMLingua/blob/main/examples/OnlineMeeting.ipynb), [CoT](https://github.com/microsoft/LLMLingua/blob/main/examples/CoT.ipynb), [Code](https://github.com/microsoft/LLMLingua/blob/main/examples/Code.ipynb), and [RAG using LlamaIndex](https://github.com/microsoft/LLMLingua/blob/main/examples/RAGLlamaIndex.ipynb).
+- 👾 LongLLMLingua is now part of the [LlamaIndex pipeline](https://github.com/run-llama/llama_index/blob/main/llama_index/postprocessor/longllmlingua.py), a widely-used RAG framework.
 
-[LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736) (EMNLP 2023).<br>
-_Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu_
+## TL;DR
 
-LongLLMLingua is a method that enhances LLMs' ability to perceive key information in long-context scenarios using prompt compression, achieveing up to $28.5 in cost savings per 1,000 samples while also improving performance.
+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.
 
-[LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://arxiv.org/abs/2310.06839) (Under Review).<br>
-_Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu_
+- [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_