---
title: "LLMs May Perform MCQA by Selecting the Least Incorrect Option"
description: "As a remedy, the authors propose MCQA+, an augmentation that mixes… 9.3 points on MCQA+. 30 references, cited by 20 papers here."
authors: ["Haochun Wang", "Sendong Zhao", "Zewen Qiang", "Nuwa Xi", "Bing Qin", "Ting Liu"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.390/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2025-llms-may-perform-mcqa-selecting/"
---
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# LLMs May Perform MCQA by Selecting the Least Incorrect Option

**Authors:** [Haochun Wang](https://md.research.elamur.ai/free/en/authors/haochun-wang/), [Sendong Zhao](https://md.research.elamur.ai/free/en/authors/sendong-zhao/), [Zewen Qiang](https://md.research.elamur.ai/free/en/authors/zewen-qiang/), [Nuwa Xi](https://md.research.elamur.ai/free/en/authors/nuwa-xi/), [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/) and [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.390/) · [PDF](https://aclanthology.org/2025.coling-main.390.pdf) · [OpenAlex](https://openalex.org/W4391556020)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [20](https://md.research.elamur.ai/free/en/papers/wang-2025-llms-may-perform-mcqa-selecting/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Benchmarks such as MMLU judge LLMs by multiple-choice accuracy. Earlier studies showed that answers can change when the options are reordered. This paper argues that a stable answer still does not prove the model knows the truth. The authors first keep only the MMLU and MedMCQA items that a model answers correctly under every permutation of options (the ♦ subsets). They then probe those items in two ways:
- turning each option into a separate true-or-false question;
- replacing the correct option with "None of the above".

Models tested include LLaMA 2/3, Mixtral 8×7B and several ChatGPT versions. All of them often accept wrong options as true and rarely pick "None of the above". This suggests they choose the least incorrect option rather than recognising one answer as uniquely right.

As a remedy, the authors propose MCQA+, an augmentation that mixes several derived question variants. Scores drop clearly under MCQA+, which suggests plain MCQA accuracy overstates real knowledge.

## Key points
- **Models and data:** LLaMA 3 8B, LLaMA 2 13B, LLaMA 3 70B, Mixtral 8×7B, ChatGPT-3.5, ChatGPT-4o-mini and ChatGPT-4o were evaluated. Questions came from MMLU (general domain) and MedMCQA (medical), limited to knowledge items rather than math or reasoning.
- **True-or-false probes:** On items built from the correct option, accuracy reached about 70–96%. On items built from wrong options, accuracy fell sharply, down to 36.8% for LLaMA 3 70B on MedMCQA♦.
- **"None of the above" probe:** When this option replaced the right answer, zero-shot accuracy on MMLU♦ ranged from 17.2% (LLaMA 2 13B) to 60.3% (ChatGPT-4o).
- **Three benchmark variants:**
  - MCQA+ combines all derived settings.
  - MCQA+ hard keeps only the hardest ones.
  - MCQA+ (×1) samples a single variant per question, so it costs no more than ordinary MCQA.
- **MCQA+ results:** On MMLU, LLaMA 3 8B fell from 75.1% to 56.8% on MCQA+. ChatGPT-4o lost 9.3 points on MCQA+ hard. The authors suspect the cause is next-token training that favours ranking options rather than treating one as exclusively correct.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 158 works · 28,841 citations

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## Cite

Cite as: Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin and Ting Liu. 2025. LLMs May Perform MCQA by Selecting the Least Incorrect Option. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2025llms,
  title = {{LLMs May Perform MCQA by Selecting the Least Incorrect Option}},
  author = {Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin and Ting Liu},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.390/}
}
```

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*Updated 30 October 2026*