# Conformal Relevance：用上下文集成统一各类合约束语言任务

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-02 08:00
- AIHOT 分数：36
- AIHOT 链接：https://aihot.news/items/cmtsutr3v01wmrob52lx0ldps
- 原文链接：https://arxiv.org/abs/2609.03005

## AI 摘要

新框架 Conformal Relevance 通过上下文示例筛选与集成构建评分函数，在保持覆盖率的同时提升简洁性，且几乎无需人工干预。研究在七项 NLP 任务上验证了该框架，并从理论上给出互补性条件与集成改进的饱和界，刻画了集成多样性对最坏情况句子评分的影响。

## 正文

Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: coverage, retaining enough pertinent information to achieve some goal, and conciseness, removing as much irrelevant information as possible. Conformal prediction methods have been used to guarantee coverage, and must be optimized for conciseness through design of a score function. State-of-the-art scoring functions use hand-engineered LLM prompts asking the model to rate the importance of content, but manual prompt engineering is labor-intensive and task-specific. We introduce the Conformal Relevance framework which uses in-context learning example curation and ensembling to create a score function which maintains coverage while improving conciseness with minimal manual input. We demonstrate this framework's application on seven NLP tasks, and also theoretically study the impact of diversity for ensembled conformal scores, giving a complementarity condition that characterizes when ensembling improves worst-case sentence scores, and a saturation bound on ensemble improvement.