# Promptception: How Sensitive Are Large Multimodal Models to Prompts?

arXiv: https://arxiv.org/abs/2509.03986 · HTML: https://mlsanity.com/paper/2509.03986/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [A Diagram Is Worth A Dozen Images](https://mlsanity.com/paper/1603.07396/) (2016)
- [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024)
- [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [Leveraging Large Language Models for Multiple Choice Question Answering](https://mlsanity.com/paper/2210.12353/) (2022)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding](https://mlsanity.com/paper/2406.14515/) (2024)
- [ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs](https://mlsanity.com/paper/2410.12405/) (2024)
- [Does Prompt Formatting Have Any Impact on LLM Performance?](https://mlsanity.com/paper/2411.10541/) (2024)
- [When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards](https://mlsanity.com/paper/2402.01781/) (2024)
- [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://mlsanity.com/paper/2406.12334/) (2024)
- [Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://mlsanity.com/paper/2402.12483/) (2024)
- [VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos](https://mlsanity.com/paper/2411.04923/) (2024)
- [Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation](https://mlsanity.com/paper/2501.03225/) (2025)
- [Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs](https://mlsanity.com/paper/2403.11755/) (2024)
- [Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?](https://mlsanity.com/paper/2307.11978/) (2023)
- [DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation](https://mlsanity.com/paper/2503.01622/) (2025)
- [Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering](https://mlsanity.com/paper/2306.09996/) (2023)

## Built on by (most cited first, 13 of 13)

- [Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systems](https://mlsanity.com/paper/2603.00131/) (2026)
- [Taming Object Hallucinations with Verified Atomic Confidence Estimation](https://mlsanity.com/paper/2511.09228/) (2025)
- [Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs](https://mlsanity.com/paper/2605.30646/) (2026)
- [Prompt-Induced Score Variance in Zero-Shot Binary Vision-Language Safety Classification](https://mlsanity.com/paper/2605.00326/) (2026)
- [CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models](https://mlsanity.com/paper/2605.17826/) (2026)
- [Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models](https://mlsanity.com/paper/2606.26079/) (2026)
- [Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation](https://mlsanity.com/paper/2607.05554/) (2026)
- [MIRROR: Learning from the Other View for Multi-Modal Reasoning](https://mlsanity.com/paper/2607.21552/) (2026)
- [PEEM: Prompt Engineering Evaluation Metrics for Interpretable Joint Evaluation of Prompts and Responses](https://mlsanity.com/paper/2603.10477/) (2026)
- [Prompts Without Evidence: How Neuroimaging Mentions Shift Clinical Vision-Language Model Predictions](https://mlsanity.com/paper/2603.28387/) (2026)
- [Towards Annotation-Free Validation of MLLMs: A Vision-Language Logical Consistency Metric](https://mlsanity.com/paper/2605.06201/) (2026)
- [Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models](https://mlsanity.com/paper/2607.15565/) (2026)
- [How Do Prompt Variations Affect Energy Consumption in On-Device LLMs?](https://mlsanity.com/paper/2609.01798/) (2026)

## Abstract (arXiv)

Despite the success of Large Multimodal Models (LMMs) in recent years, prompt design for LMMs in Multiple-Choice Question Answering (MCQA) remains poorly understood. We show that even minor variations in prompt phrasing and structure can lead to accuracy deviations of up to 15% for certain prompts and models. This variability poses a challenge for transparent and fair LMM evaluation, as models often report their best-case performance using carefully selected prompts. To address this, we introduce Promptception, a systematic framework for evaluating prompt sensitivity in LMMs. It consists of 61 prompt types, spanning 15 categories and 6 supercategories, each targeting specific aspects of prompt formulation, and is used to evaluate 10 LMMs ranging from lightweight open-source models to GPT-4o and Gemini 1.5 Pro, across 3 MCQA benchmarks: MMStar, MMMU-Pro, MVBench. Our findings reveal that proprietary models exhibit greater sensitivity to prompt phrasing, reflecting tighter alignment with instruction semantics, while open-source models are steadier but struggle with nuanced and complex phrasing. Based on this analysis, we propose Prompting Principles tailored to proprietary and open-source LMMs, enabling more robust and fair model evaluation.