MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention Heads

Weihao Liu; Ning Wu; Shiping Yang; Wenbiao Ding; Shining Liang; Ming Gong; Dongmei Zhang

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arXiv · 2502.13963

MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention Heads

Weihao Liu·Ning Wu·Shiping Yang·Wenbiao Ding·Shining Liang·Ming Gong·Dongmei Zhang

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Abstract

Large Language Models (LLMs) frequently show distracted attention due to irrelevant information in the input, which severely impairs their long-context capabilities. Inspired by recent studies on the effectiveness of retrieval heads in long-context factutality, we aim at addressing this distraction issue through improving such retrieval heads directly. We propose Multi-Document Attention Focusing (MuDAF), a novel method that explicitly optimizes the attention distribution at the head level through contrastive learning. According to the experimental results, MuDAF can significantly improve the long-context question answering performance of LLMs, especially in multi-document question answering. Extensive evaluations on retrieval scores and attention visualizations show that MuDAF possesses great potential in making attention heads more focused on relevant information and reducing attention distractions.

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Weihao Liu, Ning Wu, Shiping Yang, Wenbiao Ding, Shining Liang, Ming Gong, Dongmei Zhang. 2025-02-19. MuDAF: Long-Context Multi-Document Attention Focusing through Contrastive Learning on Attention Heads. https://arxiv.org/abs/2502.13963

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