English(EN) Improving Selective Classification with Pairwise Queries for Binary Classification

LLM选择性分类通过成对查询得到改进

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-01 04:00

研究人员开发了一种用于二元任务选择性分类的新方法，该方法特别适用于执行上下文学习的大型语言模型（LLM）。该技术涉及向同一模型提出额外的成对查询，以识别高错误样本，从而减少非拒绝数据点的预测错误。通过在合成和真实数据集上进行的实验证明，与仅依赖原始置信度估计相比，该方法旨在改善准确性-成本权衡。 AI

影响通过改进置信度估计和减少不确定预测的错误，增强了 LLM 在分类任务中的可靠性。

排序理由该集群包含一篇详细介绍选择性分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍选择性分类新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 121 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Harsh Vardhan, Sunav Choudhary, Natwar Modani, Arya Mazumdar · 2026-06-01 04:00

使用成对查询改进二元分类中的选择性分类

arXiv:2605.30615v1 Announce Type: new Abstract: In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which it is not confident. The rejected samples are often labeled by an expert, which…

本文如何被排名

报道来源 [1]

使用成对查询改进二元分类中的选择性分类

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