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Make Your LLM Fully Utilize the Context
April 26, 2024, 4:47 a.m. | Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou
cs.CL updates on arXiv.org arxiv.org
Abstract: While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge. We hypothesize that it stems from insufficient explicit supervision during the long-context training, which fails to emphasize that any position in a long context can hold crucial information. Based on this intuition, our study presents information-intensive (IN2) training, a purely data-driven solution to overcome lost-in-the-middle. Specifically, IN2 training …
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