arXiv cs.AI· Xiang Liu, Haochen Ding·· 1 天前AI 评分24
面向不同查询的文本片段在线证据包含关系预测（OECP）
Online Evidence-Containment Prediction over Text Spans under Distinct Queries
AI 导读
研究者将嵌套文本区域的顺序验证形式化为 Online Evidence-Containment Prediction（OECP），即不同查询下的在线等渗回归，最坏情况 regret 为近线性的 Θ̃(m_eff)。
正文 · 原文
Abstract:Evidence-grounded NLP systems often verify nested text regions sequentially, so verifying one region constrains others. We formalize this as Online Evidence-Containment Prediction (OECP): each candidate is predicted before exact verification and queried at most once. Span containment is exactly a triangular 2D product order, making OECP distinct-query online isotonic regression. With multiple admissible targets, worst-case regret is near-linear, $\widetilde{\Theta}(m_{\mathrm{eff}})$, in the effective scale given by endpoint-rank compression; a single fixed target, as in standard QA, admits an explicit polynomial-time learner with optimal $\Theta(\log m_{\mathrm{eff}})$ regret, run on real NQ episodes. On Natural Questions, containment yields a large deductive gain plus a smaller learned gain on unresolved candidates, reproduced across two backbones and DPR hierarchies. Expected Search-space Reduction (ESR) turns these probabilities into decisions, localizing evidence in fewer verifier calls on SciFact, ERASER, and DPR hierarchies; on DPR hierarchies under call budgets it outperforms four recent external selectors and raises fixed-reader F1 over learned and generator-greedy scheduling.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.41384 [cs.AI] |
| (or arXiv:2610.41384v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41384 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xiang Liu [view email]
[v1]
Fri, 30 Oct 2026 07:47:30 UTC (2,187 KB)
来源：arXiv cs.AI · arxiv.org