# VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning

arXiv: https://arxiv.org/abs/2403.13164 · HTML: https://mlsanity.com/paper/2403.13164/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 69 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 30 of 49)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2308.02490/) (2023)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models](https://mlsanity.com/paper/2308.01390/) (2023)
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://mlsanity.com/paper/2305.03726/) (2023)

## Built on by (most cited first, 30 of 36)

- [MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning](https://mlsanity.com/paper/2409.20566/) (2024)
- [TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration](https://mlsanity.com/paper/2505.17098/) (2025)
- [LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation](https://mlsanity.com/paper/2412.05148/) (2024)
- [CATP: Contextually Adaptive Token Pruning for Efficient and Enhanced Multimodal In-Context Learning](https://mlsanity.com/paper/2508.07871/) (2025)
- [QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding](https://mlsanity.com/paper/2604.25884/) (2026)
- [Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations](https://mlsanity.com/paper/2503.04839/) (2025)
- [SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning](https://mlsanity.com/paper/2506.21355/) (2025)
- [Generalizable Object Re-Identification via Visual In-Context Prompting](https://mlsanity.com/paper/2508.21222/) (2025)
- [CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?](https://mlsanity.com/paper/2502.11300/) (2025)
- [Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning](https://mlsanity.com/paper/2502.20587/) (2025)
- [PISA-Bench: The PISA Index as a Multilingual and Multimodal Metric for the Evaluation of Vision-Language Models](https://mlsanity.com/paper/2510.24792/) (2025)
- [EgoHandICL: Egocentric 3D Hand Reconstruction with In-Context Learning](https://mlsanity.com/paper/2601.19850/) (2026)
- [Personal Visual Context Learning in Large Multimodal Models](https://mlsanity.com/paper/2605.10936/) (2026)
- [UniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy](https://mlsanity.com/paper/2603.24690/) (2026)
- [Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models](https://mlsanity.com/paper/2411.06287/) (2024)
- [ArtifactLens: Hundreds of Labels Are Enough for Artifact Detection with VLMs](https://mlsanity.com/paper/2602.09475/) (2026)
- [Parallel In-context Learning for Large Vision Language Models](https://mlsanity.com/paper/2603.16092/) (2026)
- [MMCL-Bench: Multimodal Context Learning from Visual Rules, Procedures, and Evidence](https://mlsanity.com/paper/2605.12703/) (2026)
- [ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis](https://mlsanity.com/paper/2404.10141/) (2024)
- [Image-Text Relation Prediction for Multilingual Tweets](https://mlsanity.com/paper/2505.05040/) (2025)
- [Meta-Adaptive Prompt Distillation for Few-Shot Visual Question Answering](https://mlsanity.com/paper/2506.06905/) (2025)
- [FewMMBench: A Benchmark for Multimodal Few-Shot Learning](https://mlsanity.com/paper/2602.21854/) (2026)
- [Dive into the Scene: Breaking the Perceptual Bottleneck in Vision-Language Decision Making via Focus Plan Generation](https://mlsanity.com/paper/2606.04046/) (2026)
- [Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks](https://mlsanity.com/paper/2606.10967/) (2026)
- [From Frames to Temporal Graphs: In-Context Egocentric Action Recognition with Vision-Language Models](https://mlsanity.com/paper/2606.15417/) (2026)
- [Criterion-Conditional In-Context Learning: Evaluating Criterion-Shift Adaptation in Vision-Language Models](https://mlsanity.com/paper/2607.02575/) (2026)
- [In-Context Collapse in Vision-Language Models and How to Mitigate it?](https://mlsanity.com/paper/2608.02830/) (2026)
- [MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning](https://mlsanity.com/paper/2608.12724/) (2026)
- [Taming Visual Neglect: A Variational Information Bottleneck Framework for Adaptive Attention in Multimodal In-Context Learning](https://mlsanity.com/paper/2608.23570/) (2026)
- [Instruction Distillation: Text Instructions as Visual Examples](https://mlsanity.com/paper/2608.28696/) (2026)

## Abstract (arXiv)

Large language models (LLMs) famously exhibit emergent in-context learning (ICL) -- the ability to rapidly adapt to new tasks using few-shot examples provided as a prompt, without updating the model's weights. Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding. However, investigations into multimodal ICL have predominantly focused on few-shot visual question answering (VQA), and image captioning, which we will show neither exploit the strengths of ICL, nor test its limitations. The broader capabilities and limitations of multimodal ICL remain under-explored. In this study, we introduce a comprehensive benchmark VL-ICL Bench for multimodal in-context learning, encompassing a broad spectrum of tasks that involve both images and text as inputs and outputs, and different types of challenges, from perception to reasoning and long context length. We evaluate the abilities of state-of-the-art VLLMs against this benchmark suite, revealing their diverse strengths and weaknesses, and showing that even the most advanced models, such as GPT-4, find the tasks challenging. By highlighting a range of new ICL tasks, and the associated strengths and limitations of existing models, we hope that our dataset will inspire future work on enhancing the in-context learning capabilities of VLLMs, as well as inspire new applications that leverage VLLM ICL. The code and dataset are available at https://github.com/ys-zong/VL-ICL.