arxiv preprint - Make Your LLM Fully Utilize the Context

In this episode, we discuss Make Your LLM Fully Utilize the Context by Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou. The paper "Make Your LLM Fully Utilize the Context" delves into solving the lost-in-the-middle challenge in large language models (LLMs), where these models fail to fully use the contextual information provided in longer texts. The authors introduce a new training technique called INformation-INtensive (IN2) aiming to enhance processing and integration of detailed information across extensive text segments up to 32,000 tokens. They implement this method in a model called FILM-7B (FILl-in-the-Middle), demonstrating its superior ability to handle long-context scenarios effectively alongside maintaining performance on shorter contexts, and showing significant improvements in tasks such as NarrativeQA.