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arXiv · 2506.17951

A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment

Quanwei Tang·Sophia Yat Mei Lee·Junshuang Wu·Dong Zhang·Shoushan Li·Erik Cambria·Guodong Zhou
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Abstract

Recent advancements in retrieval-augmented generation (RAG) have enhanced large language models in question answering by integrating external knowledge. However, challenges persist in achieving global understanding and aligning responses with human ethical and quality preferences. To address these issues, we propose GraphMPA, a comprehensive graph-based framework with mode-seeking preference alignment. Our approach constructs a hierarchical document graph using a general similarity measurement, mimicking human cognitive processes for information understanding and synthesis. Additionally, we introduce mode-seeking preference optimization to better align model outputs with human preferences through probability-matching constraints. Extensive experiments on six datasets demonstrate the effectiveness of our \href{https://github.com/tangquanwei/GraphMPA}{GraphMPA}.

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Quanwei Tang, Sophia Yat Mei Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou. 2025-06-22. A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment. https://arxiv.org/abs/2506.17951

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