# A Study on Large Language Models' Limitations in Multiple-Choice Question Answering

arXiv: https://arxiv.org/abs/2401.07955 · HTML: https://mlsanity.com/paper/2401.07955/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 55 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://mlsanity.com/paper/2308.09583/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://mlsanity.com/paper/2012.00955/) (2020)
- [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://mlsanity.com/paper/2306.02707/) (2023)
- [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://mlsanity.com/paper/2308.11483/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://mlsanity.com/paper/2212.12017/) (2022)
- [Leveraging Large Language Models for Multiple Choice Question Answering](https://mlsanity.com/paper/2210.12353/) (2022)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [Evaluating and Inducing Personality in Pre-trained Language Models](https://mlsanity.com/paper/2206.07550/) (2022)
- [A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology](https://mlsanity.com/paper/2308.04709/) (2023)
- [Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording](https://mlsanity.com/paper/2306.06199/) (2023)
- [Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy](https://mlsanity.com/paper/2305.14596/) (2023)
- [The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition](https://mlsanity.com/paper/2212.06346/) (2022)

## Built on by (most cited first, 22 of 22)

- [OLMES: A Standard for Language Model Evaluations](https://mlsanity.com/paper/2406.08446/) (2024)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://mlsanity.com/paper/2502.14127/) (2025)
- [Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions](https://mlsanity.com/paper/2407.15018/) (2024)
- [From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered](https://mlsanity.com/paper/2506.07461/) (2025)
- [PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations](https://mlsanity.com/paper/2405.19740/) (2024)
- [Evaluating LLM Reasoning in the Operations Research Domain with ORQA](https://mlsanity.com/paper/2412.17874/) (2024)
- [Searching for Structure: Investigating Emergent Communication with Large Language Models](https://mlsanity.com/paper/2412.07646/) (2024)
- [Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communication](https://mlsanity.com/paper/2503.04395/) (2025)
- [Pragmatic Competence Evaluation of Large Language Models for the Korean Language](https://mlsanity.com/paper/2403.12675/) (2024)
- [Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems](https://mlsanity.com/paper/2510.02377/) (2025)
- [Revisiting NLI: Towards Cost-Effective and Human-Aligned Metrics for Evaluating LLMs in Question Answering](https://mlsanity.com/paper/2511.07659/) (2025)
- [Assessing Language Models' Worldview for Fiction Generation](https://mlsanity.com/paper/2408.07904/) (2024)
- [Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino](https://mlsanity.com/paper/2409.15380/) (2024)
- [Exploring Language Model Generalization in Low-Resource Extractive QA](https://mlsanity.com/paper/2409.18446/) (2024)
- [La Leaderboard: A Large Language Model Leaderboard for Spanish Varieties and Languages of Spain and Latin America](https://mlsanity.com/paper/2507.00999/) (2025)
- [Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark](https://mlsanity.com/paper/2602.16811/) (2026)
- [When Choices Become Risks: Safety Failures of Large Language Models under Multiple-Choice Constraints](https://mlsanity.com/paper/2604.16916/) (2026)
- [Listening to the Wise Few: Query-Key Alignment Unlocks Latent Correct Answers in Large Language Models](https://mlsanity.com/paper/2410.02343/) (2024)
- [Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons](https://mlsanity.com/paper/2505.23477/) (2025)
- [Token Constraint Decoding Improves Robustness on Question Answering for Large Language Models](https://mlsanity.com/paper/2506.09408/) (2025)
- [Measuring Stability Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology](https://mlsanity.com/paper/2601.11567/) (2025)

## Abstract (arXiv)

The widespread adoption of Large Language Models (LLMs) has become commonplace, particularly with the emergence of open-source models. More importantly, smaller models are well-suited for integration into consumer devices and are frequently employed either as standalone solutions or as subroutines in various AI tasks. Despite their ubiquitous use, there is no systematic analysis of their specific capabilities and limitations. In this study, we tackle one of the most widely used tasks - answering Multiple Choice Question (MCQ). We analyze 26 small open-source models and find that 65% of the models do not understand the task, only 4 models properly select an answer from the given choices, and only 5 of these models are choice order independent. These results are rather alarming given the extensive use of MCQ tests with these models. We recommend exercising caution and testing task understanding before using MCQ to evaluate LLMs in any field whatsoever.