metadata

title: Llmlingua 2
emoji: 💻
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 4.21.0
app_file: app.py
pinned: false
license: cc-by-nc-sa-4.0

LLMLingua-2 is a branch of work from project:

LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression

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 (EMNLP 2023)
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 (ICLR ME-FoMo 2024)
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 (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