让对话搜索重写更智能，兼顾检索与回复反馈。
Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
- 从重写、检索、回复三方面构建自洽偏好数据
- 在分布内外测试中均显著提升重写效果
- 适合做对话系统优化与多轮搜索研究者
对话查询重写（CQR）旨在将模糊查询改写为更高效的搜索表达。早期研究多孤立进行重写，忽略了重写结果对检索和回复的反馈影响。为此，我们提出多维度自洽偏好对齐的对话查询重写方法（MSPA-CQR）。首先，从重写、检索、回复三个维度构建自洽偏好对齐数据，生成更多样化的重写查询；其次，采用前缀引导的多维度直接偏好优化方法，从三个不同维度学习偏好信息。实验结果表明，MSPA-CQR在分布内与分布外场景下均表现优异。
原文摘要 · Abstract (English)
Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominantly focused on the rewriting in isolation, ignoring the feedback from query rewrite, passage retrieval and response generation in the rewriting process. To address this issue, we propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR). Specifically, we first construct self-consistent preference alignment data from three dimensions (rewriting, retrieval, and response) to generate more diverse rewritten queries. Then we propose prefix guided multi-faceted direct preference optimization to learn preference information from three different dimensions. The experimental results show that our MSPA-CQR is effective in both in- and out-of-distribution scenarios.
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