Penalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices
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Computer Science > Computation and Language
arXiv:2610.08153 (cs)
[Submitted on 6 Oct 2026]
Title:Penalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices
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Abstract:Multiple-choice question answering (MCQA) is commonly used to evaluate large language models under the assumption that one of the provided options is correct, typically using answer-selection accuracy. However, in real deployments, users or retrieval systems may provide invalid option sets in which none of the listed choices is correct, and selecting one of them may incur downstream cost. We study this setting as penalty-framed no-valid-option MCQA. Using the mathematics subset of MMLU-Pro, we remove the labeled correct option, allow models to either choose a remaining option or output ABSTAIN, and penalize invalid forced-choice responses. We further introduce correct-conditioned analysis, evaluating abstention only on instances that the model originally answered correctly. Experiments show that high MCQA accuracy does not fully guarantee abstention reliability: even under explicit no-valid-option-aware instructions and penalty-based scoring, models still produce invalid forced-choice responses for a subset of originally correct instances. These results show that penalty-framed no-valid-option MCQA reveals an aspect of model reliability not captured by standard answer-selection accuracy.
| Comments: | Accepted to AACL-IJCNLP 2026 Main Conference (Short Paper) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08153 [cs.CL] |
| (or arXiv:2610.08153v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08153
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Penalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices, by Jinhyeok Kim and 1 other authors
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