无需参考文本也能优化对话查询重写，效果接近有参考时的水平。
References Indeed Matter? Reference-Free Preference Optimization for Conversational Query Reformulation
- 用对话中的回复生成伪参考文本，替代真实参考。
- 在无参考条件下仍达到96.9%~99.1%的检索准确率。
- 适合缺乏标注参考数据的对话系统实际部署场景。
对话查询重写（CQR）已成为提升基于对话的应用中检索性能的关键技术。然而，现有方法通常依赖参考段落进行优化，这在真实场景中难以获取。为此，我们提出一种全新的无参考偏好优化框架DualReform，该框架从仅包含查询与回复的常见对话数据集中生成伪参考段落。DualReform通过两项关键创新实现：(1) 响应推理，即利用回复作为代理推断伪参考段落；(2) 双角色响应优化，即利用CQR模型在响应优化与查询重写之间的共同目标进行双向优化。尽管不依赖真实参考段落，DualReform在检索准确率上达到仅使用参考段落时的96.9%–99.1%，并相较当前最优方法提升高达31.6%。
原文摘要 · Abstract (English)
Conversational query reformulation (CQR) has become indispensable for improving retrieval in dialogue-based applications. However, existing approaches typically rely on reference passages for optimization, which are impractical to acquire in real-world scenarios. To address this limitation, we introduce a novel reference-free preference optimization framework DualReform that generates pseudo reference passages from commonly-encountered conversational datasets containing only queries and responses. DualReform attains this goal through two key innovations: (1) response-based inference, where responses serve as proxies to infer pseudo reference passages, and (2) response refinement via the dual-role of CQR, where a CQR model refines responses based on the shared objectives between response refinement and CQR. Despite not relying on reference passages, DualReform achieves 96.9--99.1% of the retrieval accuracy attainable only with reference passages and surpasses the state-of-the-art method by up to 31.6%.
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