Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering (2025)

Abstract: One of the most widely used tasks for evaluating Large Language Models (LLMs) is Multiple-Choice Question Answering (MCQA). While open-ended question answering tasks are more challenging to evaluate, MCQA tasks are, in principle, easier to assess, as the model’s answer is thought to be simple to extract and is compared directly to a set of predefined choices. However, recent studies have started to question the reliability of MCQA evaluation, showing that multiple factors can significantly impact the reported performance of LLMs, especially when the model generates free-form text before selecting one of the answer choices. In this work, we shed light on the inconsistencies of MCQA evaluation strategies, which can lead to inaccurate and misleading model comparisons. We systematically analyze whether existing answer extraction methods are aligned with human judgment, and how they are influenced by answer constraints in the prompt across different domains. Our experiments demonstrate that traditional evaluation strategies often underestimate LLM capabilities, while LLM-based answer extractors are prone to systematic errors. Moreover, we reveal a fundamental trade-off between including format constraints in the prompt to simplify answer extraction and allowing models to generate free-form text to improve reasoning. Our findings call for standardized evaluation methodologies and highlight the need for more reliable and consistent MCQA evaluation practices.

## introduction mcqa is one of the most common tasks used to evaluate llms across various domains, including ## related work this section surveys previous research in mcqa evaluation on two main aspects: how task format and inherent biases affect model performance (section 2.1), and how different strategies for answer extraction influence evaluation (section 2.2). ## methodology our investigation into mcqa evaluation reliability covers three dimensions: evaluation strategies, prompt settings, and benchmark domains. for each dimension, we design controlled experiments that isolate specific variables while maintaining others constant, allowing us to measure: in the following sections, we first formalize the mcqa task and our evaluation framework (section 3.1), then outline the evaluation strategies under investigation (section 3.2), and finally describe our prompt settings (section 3.3). ## can llm-based answer extractors solve the mcqa task? our analysis in section 5 uncovered discrepancies between xfinder's outputs and human annotations. additionally, in cases where the annotators labeled responses as "[no valid answer]", we found that, in some instances, xfinder still assigned one of the available labels. this suggests that xfinder may inherit biases from its underlying base model, occasionally attempting to solve the mcqa task rather than strictly adhering to the intended answer extraction objective for which it was fine-tuned. to test this hypothesis, we designed three distinct prompts to highlight this unintended behavior (appendix a.8) . the rationale behind these prompts is the creation of an ambiguous answer that tries to stimulate the answer extractor model to solve the mcqa task. according to xfinder's design principles, all these prompts should result in a "[no valid answer]" response, since no single answer is explicitly deemed as correct. to quantify the effects that these prompts have on the behavior of xfinder, we designed two metrics: the adversarial rate and the relative accuracy. the adversarial rate is defined as the percentage of instances where xfinder assigns one of the available labels instead of generating "[no valid answer]", while the relative accuracy is defined as the percentage of cases where xfinder selects a label that correctly matches the ground truth for a given sample in the dataset. the results in ## conclusions in this paper, we analyzed the evaluation of large language models in multiple-choice question answering, examining the impact of evaluation strategies, prompt constraints, and benchmark domains on model performance. our findings show that traditional regex-based and first-token probability our analyses -which corroborate the lesson of @xcite -highlight the need for standardized evaluation protocols in order to mitigate biases introduced by prompt constraints and answer extraction techniques. we hope that our findings may help researchers establish more accurate, fair, and reliable model assessments.