Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection
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Computer Science > Computation and Language
arXiv:2610.13821 (cs)
[Submitted on 9 Oct 2026]
Title:Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection
Authors:Juan Terven, Diana-Margarita Córdova-Esparza, Ruben Gomez, Carlos Miranda Rocha, Julio-Alejandro Romero-González, Edgar Chavez-Urbiola
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Abstract: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.
| Comments: | 17 pages, 2 figures, 10 tables |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.2.7; I.2.6 |
| Cite as: | arXiv:2610.13821 [cs.CL] |
| (or arXiv:2610.13821v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13821
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection, by Juan Terven and 5 other authors
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