Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention

AI Digest

文章揭示多跳检索失败具有结构可预测性，提出CWAR可减少性和特征互补性理论，并通过RCS置信度评分实现校准弃权策略，提升模型可靠性。

The paper identifies structurally predictable multi-hop retrieval failures, proposes two theoretical results, and demonstrates RCS-based abstention improves model reliability across benchmarks.

Key points

• 多跳检索失败集中于可预测的子群体，非均匀分布 Failures cluster in structurally predictable subpopulations
• CWAR可减少性理论解释LLM-judge与密集检索的性能差异 CWAR reducibility explains performance gaps between regimes
• RCS融合九项结构特征实现跨数据集的校准弃权策略 RCS combines nine structural features for cross-dataset abstention

Takeaway: 结构化置信度评分与弃权策略可显著提升多跳检索模型的鲁棒性。 / Structured confidence scoring and abstention enhance multi-hop retrieval robustness.

Why it matters 为多跳检索模型的鲁棒性提升提供了理论框架和实用方法，具有实际应用价值。