LMSYS-Chat-1M：一个大规模真实世界大语言模型对话数据集
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
AI总结：
本文发布包含百万条真实LLM对话的LMSYS-Chat-1M数据集，介绍其采集与特征，并通过内容审核、安全基准等四个用例验证其价值，为LLM研究提供重要资源。
摘要：
随着大语言模型（LLM）在各类应用中广泛使用，研究人们在真实场景中如何与大语言模型交互变得愈发重要。本文提出了LMSYS-Chat-1M这一大规模数据集，其中包含与25个前沿LLM的一百万条真实世界对话。该数据集从我们的Vicuna演示站点和Chatbot Arena网站的野外环境中，采集自21万个独立IP地址。我们概述了数据集的内容，包括其整理流程、基础统计信息和主题分布，凸显了其多样性、原创性与规模优势。我们通过四个用例验证了它的通用性：开发性能与GPT-4相当的内容审核模型、构建安全基准、训练性能与Vicuna相当的指令遵循模型，以及生成有难度的基准测试问题。我们认为该数据集将成为理解并推进LLM能力的宝贵资源，数据集已公开于https://huggingface.co/datasets/lmsys/lmsys-chat-1m。
英文摘要：
Studying how people interact with large language models (LLMs) in real-world scenarios is increasingly important due to their widespread use in various applications. In this paper, we introduce LMSYS-Chat-1M, a large-scale dataset containing one million real-world conversations with 25 state-of-the-art LLMs. This dataset is collected from 210K unique IP addresses in the wild on our Vicuna demo and Chatbot Arena website. We offer an overview of the dataset's content, including its curation process, basic statistics, and topic distribution, highlighting its diversity, originality, and scale. We demonstrate its versatility through four use cases: developing content moderation models that perform similarly to GPT-4, building a safety benchmark, training instruction-following models that perform similarly to Vicuna, and creating challenging benchmark questions. We believe that this dataset will serve as a valuable resource for understanding and advancing LLM capabilities. The dataset is publicly available at https://huggingface.co/datasets/lmsys/lmsys-chat-1m.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
