The paper "Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning" addresses the challenge of performing many-shot multimodal in-context learning (ICL) in large multimodal models (LMMs). The authors propose Multimodal Task Vectors (MTV), a method that leverages compact implicit representations of in-context examples compressed into the model's attention heads. This approach enables LMMs to handle a large number of multimodal examples without being limited by the pretraining context length, which is a significant limitation in multimodal ICL settings. The key contributions of the paper are: 1. **Existence of MTV**: The authors demonstrate the existence of MTV in LMMs and show how they can be used to compress many-shot multimodal ICL examples. 2. **Many-Shot ICL Performance**: MTV enables LMMs to perform many-shot multimodal ICL on various vision-and-language tasks, outperforming zero-shot and few-shot ICL settings. 3. **Generalization**: MTV can scale to larger numbers of examples and generalize to similar out-of-domain tasks without additional context length for inference. The method involves three steps: 1. **Calculate Mean Activations**: Compute the mean activations of the attention heads across multiple inference iterations. 2. **Extract Attention Head Locations**: Select a set of attention head locations that best align with the downstream task using an adapted version of the REINFORCE algorithm. 3. **Apply MTV for Inference**: Replace the mean activation values with the selected attention head locations during inference. The paper evaluates MTV on three popular interleaved LMMs (QwenVL, Idemics2-8B, and ViLA-1.5-8B) and demonstrates its effectiveness on various vision-and-language tasks. Results show that MTV can scale to more examples, work effectively with explicit few-shot examples, and generalize to similar tasks. Additionally, MTV is more efficient in terms of runtime and memory compared to traditional few-shot ICL methods. The authors conclude that MTV is a viable solution for handling complex vision-language tasks and highlights its potential for future research in many-shot multimodal ICL.The paper "Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning" addresses the challenge of performing many-shot multimodal in-context learning (ICL) in large multimodal models (LMMs). The authors propose Multimodal Task Vectors (MTV), a method that leverages compact implicit representations of in-context examples compressed into the model's attention heads. This approach enables LMMs to handle a large number of multimodal examples without being limited by the pretraining context length, which is a significant limitation in multimodal ICL settings. The key contributions of the paper are: 1. **Existence of MTV**: The authors demonstrate the existence of MTV in LMMs and show how they can be used to compress many-shot multimodal ICL examples. 2. **Many-Shot ICL Performance**: MTV enables LMMs to perform many-shot multimodal ICL on various vision-and-language tasks, outperforming zero-shot and few-shot ICL settings. 3. **Generalization**: MTV can scale to larger numbers of examples and generalize to similar out-of-domain tasks without additional context length for inference. The method involves three steps: 1. **Calculate Mean Activations**: Compute the mean activations of the attention heads across multiple inference iterations. 2. **Extract Attention Head Locations**: Select a set of attention head locations that best align with the downstream task using an adapted version of the REINFORCE algorithm. 3. **Apply MTV for Inference**: Replace the mean activation values with the selected attention head locations during inference. The paper evaluates MTV on three popular interleaved LMMs (QwenVL, Idemics2-8B, and ViLA-1.5-8B) and demonstrates its effectiveness on various vision-and-language tasks. Results show that MTV can scale to more examples, work effectively with explicit few-shot examples, and generalize to similar tasks. Additionally, MTV is more efficient in terms of runtime and memory compared to traditional few-shot ICL methods. The authors conclude that MTV is a viable solution for handling complex vision-language tasks and highlights its potential for future research in many-shot multimodal ICL.