How Long Can Context Length of Open-Source LLMs truly Promise?

Dacheng Li, Rulin Shao, Anze Xie, Ying Sheng, Lianmin Zheng, Joseph Gonzalez, Ion Stoica, Xuezhe Ma, Hao Zhang

Keywords: Long-context evaluation, long-context instruction following chatbot

Abstract: Large language models (LLMs) with long-context instruction following ability has unlocked new potentials, such as supporting long interactive chat sessions. In this paper, we introduce a test suite, LongEval, which enables us to evaluate the long-range retrieval ability of LLMs at various context lengths. We use LongEval to evaluate open-sourced LLMs, and surprisingly, we find many of them fail to achieve their promised context length. In addition, we present a recipe to fine tune a long-context chatbot based on LLaMA models, and introduce LongChat models that supporting conversations of up to 16,384 tokens. We have released our code at https://github.com/DachengLi1/LongChat.

Submission Number: 79