用实体引导注意力，让模型像人一样抓重点。
REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
- 以实体为骨架，动态引导注意力聚焦关键内容。
- 在三个数据集上超越基线，最高提升108%。
- 适合需要精准理解长文档的检索任务。
当前神经重排序模型在处理复杂信息需求和长篇内容丰富文档时表现不佳。根本问题不在于计算能力，而在于智能内容选择：如何从冗长多面的文本中识别出重要信息。人类会自然围绕关键实体和概念理解内容，而神经模型受限于固定长度的词元窗口，将所有交互视为同等重要，忽略了关键语义信号。本文提出REGENT，一种模仿人类理解方式的神经重排序模型，利用实体作为“语义骨架”引导注意力。REGENT将相关性引导直接整合到注意力机制中，结合细粒度词汇匹配与高层次语义推理。这种相关性引导注意力使模型能聚焦概念重要内容，同时保持对精确词项匹配的敏感性。REGENT在三个挑战性数据集上达到新最优性能，相比BM25最高提升108%，并持续优于ColBERT和RankVicuna等强基线。据我们所知，这是首个成功将实体语义直接融入神经注意力的工作，确立了面向实体感知的信息检索新范式。
原文摘要 · Abstract (English)
Current neural re-rankers often struggle with complex information needs and long, content-rich documents. The fundamental issue is not computational--it is intelligent content selection: identifying what matters in lengthy, multi-faceted texts. While humans naturally anchor their understanding around key entities and concepts, neural models process text within rigid token windows, treating all interactions as equally important and missing critical semantic signals. We introduce REGENT, a neural re-ranking model that mimics human-like understanding by using entities as a "semantic skeleton" to guide attention. REGENT integrates relevance guidance directly into the attention mechanism, combining fine-grained lexical matching with high-level semantic reasoning. This relevance-guided attention enables the model to focus on conceptually important content while maintaining sensitivity to precise term matches. REGENT achieves new state-of-the-art performance in three challenging datasets, providing up to 108% improvement over BM25 and consistently outperforming strong baselines including ColBERT and RankVicuna. To our knowledge, this is the first work to successfully integrate entity semantics directly into neural attention, establishing a new paradigm for entity-aware information retrieval.
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