LMSYS-Chat-1M：一个大规模真实世界的LLM对话数据集

LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset

September 21, 2023

作者: Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric. P Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang

cs.AI

摘要

随着大型语言模型（LLMs）在各种应用中的广泛使用，研究人们如何在现实场景中与其互动变得愈发重要。本文介绍了 LMSYS-Chat-1M，一个包含一百万个与 25 个最先进的LLMs 进行的真实对话的大规模数据集。该数据集是从我们的Vicuna演示和Chatbot Arena网站上210K个独特IP地址的真实环境中收集而来。我们概述了数据集的内容，包括其策划过程、基本统计数据和主题分布，突出了其多样性、独创性和规模。我们通过四个用例展示了其多样性：开发类似于GPT-4的内容管理模型、构建安全基准、训练类似于Vicuna的指令遵循模型以及创建具有挑战性的基准问题。我们相信这一数据集将成为理解和推进LLMs能力的宝贵资源。该数据集可在以下网址公开获取：https://huggingface.co/datasets/lmsys/lmsys-chat-1m。

English

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.