# Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning

arXiv: https://arxiv.org/abs/2406.15334 · HTML: https://mlsanity.com/paper/2406.15334/

## Key facts

- Cited by 50 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 86 of its 104 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## 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 86)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [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)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)

## Built on by (most cited first, 30 of 50)

- [A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models](https://mlsanity.com/paper/2502.17516/) (2025)
- [In-Context Learning Enables Robot Action Prediction in LLMs](https://mlsanity.com/paper/2410.12782/) (2024)
- [Representation Engineering for Large-Language Models: Survey and Research Challenges](https://mlsanity.com/paper/2502.17601/) (2025)
- [Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence](https://mlsanity.com/paper/2502.09927/) (2025)
- [Analyzing Finetuning Representation Shift for Multimodal LLMs Steering](https://mlsanity.com/paper/2501.03012/) (2025)
- [Vision-Language Models Create Cross-Modal Task Representations](https://mlsanity.com/paper/2410.22330/) (2024)
- [Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations](https://mlsanity.com/paper/2511.22697/) (2025)
- [Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation](https://mlsanity.com/paper/2412.01027/) (2024)
- [MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning](https://mlsanity.com/paper/2505.16225/) (2025)
- [Mimic In-Context Learning for Multimodal Tasks](https://mlsanity.com/paper/2504.08851/) (2025)
- [Enhancing Few-Shot Vision-Language Classification with Large Multimodal Model Features](https://mlsanity.com/paper/2412.00142/) (2024)
- [Do different prompting methods yield a common task representation in language models?](https://mlsanity.com/paper/2505.12075/) (2025)
- [ContextNav: Towards Agentic Multimodal In-Context Learning](https://mlsanity.com/paper/2510.04560/) (2025)
- [When Continue Learning Meets Multimodal Large Language Model: A Survey](https://mlsanity.com/paper/2503.01887/) (2025)
- [Understanding Task Vectors in In-Context Learning: Emergence, Functionality, and Limitations](https://mlsanity.com/paper/2506.09048/) (2025)
- [Just-in-time and distributed task representations in language models](https://mlsanity.com/paper/2509.04466/) (2025)
- [ELICIT: LLM Augmentation via External In-Context Capability](https://mlsanity.com/paper/2410.09343/) (2024)
- [Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations](https://mlsanity.com/paper/2503.04839/) (2025)
- [Efficient Multi-modal Long Context Learning for Training-free Adaptation](https://mlsanity.com/paper/2505.19812/) (2025)
- [Teaching VLMs to Localize Specific Objects from In-context Examples](https://mlsanity.com/paper/2411.13317/) (2024)
- [Activation Reward Models for Few-Shot Model Alignment](https://mlsanity.com/paper/2507.01368/) (2025)
- [Adaptive Task Vectors for Large Language Models](https://mlsanity.com/paper/2506.03426/) (2025)
- [Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers](https://mlsanity.com/paper/2601.20796/) (2026)
- [Unlocking In-Context Learning for Natural Datasets Beyond Language Modelling](https://mlsanity.com/paper/2501.06256/) (2025)
- [Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation](https://mlsanity.com/paper/2509.05226/) (2025)
- [Where and What Matters: Sensitivity-Aware Task Vectors for Many-Shot Multimodal In-Context Learning](https://mlsanity.com/paper/2511.08246/) (2025)
- [Retrieving Counterfactuals Improves Visual In-Context Learning](https://mlsanity.com/paper/2603.16737/) (2026)
- [MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation](https://mlsanity.com/paper/2502.11246/) (2025)
- [FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design](https://mlsanity.com/paper/2506.13066/) (2025)
- [Why Multimodal In-Context Learning Lags Behind? Unveiling the Inner Mechanisms and Bottlenecks](https://mlsanity.com/paper/2604.13403/) (2026)

## Abstract (arXiv)

The recent success of interleaved Large Multimodal Models (LMMs) in few-shot learning suggests that in-context learning (ICL) with many examples can be promising for learning new tasks. However, this many-shot multimodal ICL setting has one crucial problem: it is fundamentally limited by the model's context length set at pretraining. The problem is especially prominent in the multimodal domain, which processes both text and images, requiring additional tokens. This motivates the need for a multimodal method to compress many shots into fewer tokens without finetuning. In this work, we enable LMMs to perform multimodal, many-shot in-context learning by leveraging Multimodal Task Vectors (MTV) -- compact implicit representations of in-context examples compressed in the model's attention heads. Specifically, we first demonstrate the existence of such MTV in LMMs and then leverage these extracted MTV to enable many-shot in-context learning for various vision-and-language tasks. Our experiments suggest that MTV can scale in performance with the number of compressed shots and generalize to similar out-of-domain tasks without additional context length for inference. Code: https://github.com/Brandon3964/MultiModal-Task-Vector