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choices-only accuracy

Choices-only accuracy is an evaluation metric in natural language processing that measures the proportion of multiple-choice questions a machine learning model answers correctly when presented solely with the candidate choices, completely omitting the question stem and context. This metric serves as a diagnostic baseline to identify dataset artifacts, option-level biases, and spurious statistical patterns within evaluation benchmarks. By determining how effectively a model can select the correct answer using only the relationships, dynamics, or linguistic cues among the options, choices-only accuracy helps researchers evaluate whether performance on a benchmark reflects genuine reading comprehension and reasoning rather than the exploitation of superficial shortcuts.

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Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger

OrganizationsAllen Institute for AIUniversity of Maryland

Why you should read this

Reveals that large language models frequently select the correct answer in multiple-choice benchmarks using only the answer options by abductively inferring missing questions and exploiting choice group dynamics rather than relying on memorization alone.

Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with choices-only prompts, where models must select the correct answer only from the choices. In three MCQA datasets and four LLMs, this prompt bests a majority baseline in 11/12 cases, with up to 0.33 accuracy gain. To help explain this behavior, we conduct an in-depth, black-box analysis on memorization, choice dynamics, and question inference. Our key findings are threefold. First, we find no evidence that the choices-only accuracy stems from memorization alone. Second, priors over individual choices do not fully explain choices-only accuracy, hinting that LLMs use the group dynamics of choices. Third, LLMs have some ability to infer a relevant question from choices, and surprisingly can sometimes even match the original question. Inferring the original question is an impressive reasoning strategy, but it cannot fully explain the high choices-only accuracy of LLMs in MCQA. Thus, while LLMs are not fully incapable of reasoning in MCQA, we still advocate for the use of stronger baselines in MCQA benchmarks, the design of robust MCQA datasets for fair evaluations, and further efforts to explain LLM decision-making.

Added

2026-09-26