Large Language Models Can Self-Improve in Long-context Reasoning

• URL: http://arxiv.org/abs/2411.08147v1
• Date: Tue, 12 Nov 2024 19:53:00 GMT
• Title: Large Language Models Can Self-Improve in Long-context Reasoning
• Authors: Siheng Li, Cheng Yang, Zesen Cheng, Lemao Liu, Mo Yu, Yujiu Yang, Wai Lam,
• Abstract summary: Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. We propose ours, an approach specifically designed for this purpose. ours achieves superior performance compared to prior approaches that depend on data produced by human experts or advanced models.
• Score: 100.52886241070907
• License:
• Abstract: Large language models (LLMs) have achieved substantial progress in processing long contexts but still struggle with long-context reasoning. Existing approaches typically involve fine-tuning LLMs with synthetic data, which depends on annotations from human experts or advanced models like GPT-4, thus restricting further advancements. To address this issue, we investigate the potential for LLMs to self-improve in long-context reasoning and propose \ours, an approach specifically designed for this purpose. This approach is straightforward: we sample multiple outputs for each question, score them with Minimum Bayes Risk, and then apply supervised fine-tuning or preference optimization based on these outputs. Extensive experiments on several leading LLMs demonstrate the effectiveness of \ours, with an absolute improvement of $4.2$ points for Llama-3.1-8B-Instruct. Furthermore, \ours achieves superior performance compared to prior approaches that depend on data produced by human experts or advanced models. We anticipate that this work will open new avenues for self-improvement techniques in long-context scenarios, which are essential for the continual advancement of LLMs.

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