# Multiple-Choice Questions are Efficient and Robust LLM Evaluators

arXiv: https://arxiv.org/abs/2405.11966 · HTML: https://mlsanity.com/paper/2405.11966/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution](https://mlsanity.com/paper/2401.03065/) (2024)
- [Leveraging Large Language Models for Multiple Choice Question Answering](https://mlsanity.com/paper/2210.12353/) (2022)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code](https://mlsanity.com/paper/2303.08033/) (2023)

## Built on by (most cited first, 28 of 28)

- [The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units](https://mlsanity.com/paper/2411.02280/) (2024)
- [LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation](https://mlsanity.com/paper/2411.09301/) (2024)
- [TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding](https://mlsanity.com/paper/2502.19400/) (2025)
- [ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding](https://mlsanity.com/paper/2411.05049/) (2024)
- [Wait, that's not an option: LLMs Robustness with Incorrect Multiple-Choice Options](https://mlsanity.com/paper/2409.00113/) (2024)
- [Reasoning Models are Test Exploiters: Rethinking Multiple-Choice](https://mlsanity.com/paper/2507.15337/) (2025)
- [Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines](https://mlsanity.com/paper/2510.07614/) (2025)
- [In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models](https://mlsanity.com/paper/2409.15454/) (2024)
- [Memorization or Reasoning? Exploring the Idiom Understanding of LLMs](https://mlsanity.com/paper/2505.16216/) (2025)
- [EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants](https://mlsanity.com/paper/2502.20309/) (2025)
- [FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs](https://mlsanity.com/paper/2509.16648/) (2025)
- [PoETa v2: Toward More Robust Evaluation of Large Language Models in Portuguese](https://mlsanity.com/paper/2511.17808/) (2025)
- [RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models](https://mlsanity.com/paper/2607.25996/) (2026)
- [EQUATOR: A Deterministic Framework for Evaluating LLM Reasoning with Open-Ended Questions. # v1.0.0-beta](https://mlsanity.com/paper/2501.00257/) (2024)
- [Domain Specific Benchmarks for Evaluating Multimodal Large Language Models](https://mlsanity.com/paper/2506.12958/) (2025)
- [Semantic Calibration Prevails Where Token Confidence Fails: Benchmarking Long-Form Scientific QA](https://mlsanity.com/paper/2602.00279/) (2026)
- [Auditing LLM Benchmarks with Item Response Theory](https://mlsanity.com/paper/2605.30504/) (2026)
- [DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMs](https://mlsanity.com/paper/2505.10013/) (2025)
- [D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model](https://mlsanity.com/paper/2504.13439/) (2025)
- [Impact of Comments on LLM Comprehension of Legacy Code](https://mlsanity.com/paper/2506.11007/) (2025)
- [Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms](https://mlsanity.com/paper/2509.20278/) (2025)
- [Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories](https://mlsanity.com/paper/2607.06648/) (2026)
- [Overwhelmed by Choice: Studying LLM Decision Making at Scale](https://mlsanity.com/paper/2609.32809/) (2026)
- [From Understanding to Generation: An Efficient Shortcut for Evaluating Language Models](https://mlsanity.com/paper/2506.03592/) (2025)
- [Towards Scalable Oversight via Partitioned Human Supervision](https://mlsanity.com/paper/2510.22500/) (2025)
- [Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMs](https://mlsanity.com/paper/2604.02230/) (2026)
- [SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation](https://mlsanity.com/paper/2605.20189/) (2026)
- [Domain-Specific Jargon in Large Language Models: A Comparative Analysis between General-Purpose and Specialist Models](https://mlsanity.com/paper/2609.13556/) (2026)

## Abstract (arXiv)

We present GSM-MC, a multiple-choice (MC) dataset constructed by collecting answers and incorrect predictions on GSM8K from 60 open-source models. Through extensive experiments, we show that LLMs' performance on the MC version of this popular benchmark is strongly correlated with their performance on the original version and is quite robust to distractor choices and option orders, while the evaluation time is reduced by a factor of up to 30. Following similar procedures, we introduce MATH-MC, constructed from MATH, and PythonIO, a new program reasoning MC dataset constructed from HumanEval and MBPP. Experimental results indicate that LLMs' performance on these MC benchmarks leaves much room for improvement. Our data and code are available at https://github.com/Geralt-Targaryen/MC-Evaluation.