# 2AFC Prompting of Large Multimodal Models for Image Quality Assessment arXiv: https://arxiv.org/abs/2402.01162 ยท HTML: https://mlsanity.com/paper/2402.01162/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models. - Builds on 18 of its 41 references that are in the map. - First posted on arXiv 2 Feb 2024. ## Placed in - Multimodal LLMs / vision-language models / [Image editing and quality assessment](https://mlsanity.com/mllm/image-editing-and-quality-assessment/) ## Builds on (most cited first, 18 of 18) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023) - [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022) - [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017) - [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019) - [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019) - [InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition](https://mlsanity.com/paper/2309.15112/) (2023) - [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023) - [Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision](https://mlsanity.com/paper/2309.14181/) (2023) - [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015) - [Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models](https://mlsanity.com/paper/2311.06783/) (2023) - [Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models](https://mlsanity.com/paper/2312.08962/) (2023) - [BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models](https://mlsanity.com/paper/2312.02896/) (2023) - [DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning](https://mlsanity.com/paper/2001.08113/) (2020) - [Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment](https://mlsanity.com/paper/2209.05321/) (2022) ## Built on by (most cited first, 20 of 20) - [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025) - [Towards Open-ended Visual Quality Comparison](https://mlsanity.com/paper/2402.16641/) (2024) - [Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare](https://mlsanity.com/paper/2405.19298/) (2024) - [SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data](https://mlsanity.com/paper/2505.20347/) (2025) - [Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning](https://mlsanity.com/paper/2506.08745/) (2025) - [A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment](https://mlsanity.com/paper/2403.10854/) (2024) - [Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs](https://mlsanity.com/paper/2409.20063/) (2024) - [Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset](https://mlsanity.com/paper/2405.18842/) (2024) - [FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models](https://mlsanity.com/paper/2404.13306/) (2024) - [LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models](https://mlsanity.com/paper/2408.14008/) (2024) - [AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Images](https://mlsanity.com/paper/2504.21308/) (2025) - [Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model](https://mlsanity.com/paper/2503.06141/) (2025) - [Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning](https://mlsanity.com/paper/2504.01655/) (2025) - [Diagnosing Corruption-Induced Reliability Failures in Vision-Language Models](https://mlsanity.com/paper/2511.19032/) (2025) - [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024) - [Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics](https://mlsanity.com/paper/2412.10594/) (2024) - [CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates](https://mlsanity.com/paper/2504.10738/) (2025) - [IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring](https://mlsanity.com/paper/2605.24553/) (2026) - [VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results](https://mlsanity.com/paper/2509.09190/) (2025) - [JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding](https://mlsanity.com/paper/2607.22783/) (2026) ## Abstract (arXiv) While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored. Here we take initial steps towards this goal by employing the two-alternative forced choice~(2AFC) prompting, as 2AFC is widely regarded as the most reliable way of collecting human opinions of visual quality. Subsequently, the global quality score of each image estimated by a particular LMM can be efficiently aggregated using the maximum a posterior estimation. Meanwhile, we introduce three evaluation criteria: consistency, accuracy, and correlation, to provide comprehensive quantifications and deeper insights into the IQA capability of five LMMs. Extensive experiments show that existing LMMs exhibit remarkable IQA ability on coarse-grained quality comparison, but there is room for improvement on fine-grained quality discrimination. The proposed dataset sheds light on the future development of IQA models based on LMMs. The codes will be made publicly available at https://github.com/h4nwei/2AFC-LMMs.