Selective QA

QA: Question Answering

Latest papers

9 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection
Oct 9, 2026Juan Terven, Diana-Margarita Córdova-Esparza, Ruben Gomez +3Selective QA LLM Abstention
CICATA Qro., Instituto Politécnico Nacional, México · Facultad de Informática, Universidad Autónoma de Querétaro, México
Before Answering: Evidence Sufficiency under Size-Matched Memory Construction
Sep 26, 2026Joyanta Jyoti Mondal, Md. Shifatul Ahsan Apurba, Mridul Banik +2Selective QA Memory-Augmented Agents
Department of Computer and Information Sciences, University of Delaware, USA · Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, USA · Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, USA +2
Cite or Decline: A Strict Course-Grounded Chatbot for STEM Lecture Videos
Sep 1, 2026S M Masrur Ahmed, Jaspal SubhlokEvidence Retrieval Evidence-Grounded Generation
University of Houston
Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
Sep 1, 2026Haruto Sato, Yuki Tanaka, Ren Nakamura +2Multi-Hop QA Evidence-Grounded Reasoning
Independent Researcher
Asymptotic Risk Calibration for Selective Question Answering
Aug 12, 2026Shufan Lin, Sijin DongQuestion Answering Selective QA
Zhangjiang University · Ibaraki University
To Answer or to Abstain: Mitigating Search-Agent Hallucinations via Abstention-Aware Reinforcement Learning
Jul 12, 2026Fengji Zhang, Tianyu Fan, Yuxiang Zheng +4Question Answering RL for LLM Agents
City University of Hong Kong · Alibaba Group
Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison
May 28, 2026Tiancheng Yang, Matthias Schonlau, Ilia SucholutskyConversational Memory Question Answering
SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation
May 5, 2026Jingxi Qiu, Zeyu Han, Cheng HuangRetrieval-Augmented Generation LLM Answer Verification
ZenWeave AI · Georgetown University · Southern Methodist University
PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
Apr 6, 2026Madhav S BaidyaSelective QA Question Answering
Indian Institute of Technology (BHU) Varanasi

Oct 9, 2026J/K move · Enter open · S save

Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection

Juan Terven, Diana-Margarita Córdova-Esparza, Ruben Gomez, Carlos Miranda Rocha+2

CICATA Qro., Instituto Politécnico Nacional, México · Facultad de Informática, Universidad Autónoma de Querétaro, México

Selective QA LLM Abstention Selective Prediction Language Model Calibration RL for Language Models

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A useful language model should know when to answer and when to say "I don't know." Can we improve that decision by training the model itself or by training a separate component to check its answers? We study both approaches on Qwen3-14B for factual questions, using a score that rewards correct answers, penalizes mistakes, and allows abstention. Across three training seeds and two held-out confidence thresholds, a learned answer selector improves threshold-averaged utility over the original model's calibrated confidence filter. On TriviaQA, mean coverage increases from 20.2% to 30.9%, while the pooled error rate among scored answers decreases from 23.0% to 14.5% under exact-match grading. The tested reinforcement-learning changes do not establish consistent gains. On NQ-Open, the selector improves threshold-averaged utility over the filter but still performs worse than always abstaining. A partial human-reviewed audit preserves the TriviaQA finding. Our results support learning to select answers in this setting while showing that reliable abstention across datasets remains unresolved.