Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps

Yijiong Yu; Yongfeng Huang; Zhixiao Qi; Wei Wang; Weifeng Liu; Ran Chen; Ji Pei

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arXiv · 2410.04422

Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps

Yijiong Yu·Yongfeng Huang·Zhixiao Qi·Wei Wang·Weifeng Liu·Ran Chen·Ji Pei

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Abstract

Long-context language models (LCLMs), characterized by their extensive context window, are becoming popular. However, despite the fact that they are nearly perfect at standard long-context retrieval tasks, our evaluations demonstrate they fail in some basic cases. Later, we find they can be well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. This result emphasizes the potential necessity of solving specific long-context tasks using long-CoT methods, while previous long-context benchmarks always ignore the necessity of long reasoning for long-context tasks and treat them as direct QA tasks.

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Yijiong Yu, Yongfeng Huang, Zhixiao Qi, Wei Wang, Weifeng Liu, Ran Chen, Ji Pei. 2024-10-06. Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps. https://arxiv.org/abs/2410.04422

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