From 2762840e24b58cd50205c91af3bb1dfc8780db5f Mon Sep 17 00:00:00 2001 From: Huiqiang Jiang Date: Wed, 20 Mar 2024 11:39:00 +0800 Subject: [PATCH] Feature(LLMLingua-2): update paper link (#112) Co-authored-by: Qianhui Wu Co-authored-by: panzs <915933979@qq.com> Co-authored-by: Xufang Luo <34053802+XufangLuo@users.noreply.github.com> Co-authored-by: Yuqing Yang --- README.md | 12 ++++++------ examples/LLMLingua2.ipynb | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 2d89810..d44b572 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ | Project Page | LLMLingua | LongLLMLingua | - LLMLingua-2 | + LLMLingua-2 | LLMLingua Demo | LLMLingua-2 Demo |

@@ -20,13 +20,13 @@ https://github.com/microsoft/LLMLingua/assets/30883354/eb0ea70d-6d4c-4aa7-8977-6 ## News -- 🦚 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). +- 🦚 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/2403.12968), visit the [project page](https://llmlingua.com/llmlingua2.html), and explore our [demo](https://huggingface.co/spaces/microsoft/LLMLingua-2). - 👾 LLMLingua has been integrated into [LangChain](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/retrievers/llmlingua.ipynb) and [LlamaIndex](https://github.com/run-llama/llama_index/blob/main/docs/examples/node_postprocessor/LongLLMLingua.ipynb), two widely-used RAG frameworks. - 🤳 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'](./examples) directory for practical applications, including [RAG](./examples/RAG.ipynb), [Online Meeting](./examples/OnlineMeeting.ipynb), [CoT](./examples/CoT.ipynb), [Code](./examples/Code.ipynb), and [RAG using LlamaIndex](./examples/RAGLlamaIndex.ipynb). +- 👨‍🦯 Explore our ['./examples'](./examples) directory for practical applications, including [LLMLingua-2](./examples/LLMLingua2.ipynb), [RAG](./examples/RAG.ipynb), [Online Meeting](./examples/OnlineMeeting.ipynb), [CoT](./examples/CoT.ipynb), [Code](./examples/Code.ipynb), and [RAG using LlamaIndex](./examples/RAGLlamaIndex.ipynb). ## TL;DR @@ -42,7 +42,7 @@ LongLLMLingua mitigates the 'lost in the middle' issue in LLMs, enhancing long-c 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)
+- [LLMLingua-2: Context-Aware Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://arxiv.org/abs/2403.12968) (Under Review)
_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_ ## 🎥 Overview @@ -107,9 +107,9 @@ If you find this repo helpful, please cite the following papers: @article{wu2024llmlingua2, title = "{LLML}ingua-2: Context-Aware Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression", author = "Zhuoshi Pan and Qianhui Wu and Huiqiang Jiang and Menglin Xia and Xufang Luo and Jue Zhang and Qingwei Lin and Victor Ruhle and Yuqing Yang and Chin-Yew Lin and H. Vicky Zhao and Lili Qiu and Dongmei Zhang", - url = "https://arxiv.org/abs/2403.", + url = "https://arxiv.org/abs/2403.12968", journal = "ArXiv preprint", - volume = "abs/2403.", + volume = "abs/2403.12968", year = "2024", } ``` diff --git a/examples/LLMLingua2.ipynb b/examples/LLMLingua2.ipynb index c20f917..44a633e 100644 --- a/examples/LLMLingua2.ipynb +++ b/examples/LLMLingua2.ipynb @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "LLMLingua-2 focuses on task-agnostic prompt compression for better generalizability and efficiency. It is 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.\n", + "LLMLingua-2 focuses on task-agnostic prompt compression for better generalizability and efficiency. It is 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.\n", "\n", "Below, We showcase the usage and compression results of LLMLingua-2 on both in-domain and out-of-domain datasets, including various tasks such as single-document QA, multi-document QA, summarization and in-context learning.\n" ]