GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models

2026-06-10 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

GRIP (Guided Retrieval of In-context Prompts) is a new learnable vision-only retrieval framework designed to enhance Multimodal In-Context Learning (M-ICL) for Large Multimodal Models (LMMs). It addresses the limitation of traditional similarity-based retrieval, which often fails to provide truly beneficial in-context examples. GRIP leverages direct feedback from LMMs to identify examples that genuinely improve predictions, employing contrastive training to distinguish useful from detrimental context. This framework consistently outperforms similarity-based methods across classification, captioning, and VQA tasks on Qwen2.5-VL-7B, showing its strongest gains in classification on Idefics2-8B. Notably, retrievers trained with feedback from one open LMM can be transferred to other models, including closed-source GPT-4o and Gemini, without requiring retraining, enabling scalable and cost-efficient M-ICL deployment.

Key takeaway

For AI Engineers optimizing Large Multimodal Model performance, GRIP offers a superior method for in-context example retrieval, moving beyond simple similarity to leverage LMM feedback directly. This approach significantly boosts M-ICL across tasks like VQA and classification, and its transferability to models like GPT-4o and Gemini enables scalable, cost-efficient deployment without retraining. You should consider integrating GRIP to enhance your LMM applications.

Key insights

GRIP uses LMM feedback to learn effective in-context example retrieval, outperforming similarity-based methods and transferring across models.

Principles

• Visual similarity does not guarantee useful M-ICL context.
• LMM feedback can guide retrieval for better performance.
• Feedback-trained retrievers are transferable across LMMs.

Method

GRIP employs a learnable vision-only retrieval framework. It uses contrastive training to distinguish beneficial from detrimental in-context examples based on LMM prediction feedback.

In practice

• Deploy GRIP for M-ICL tasks like VQA or classification.
• Train retrievers once for use across diverse LMMs.
• Prioritize feedback-guided over pure similarity retrieval.

Topics

• Large Multimodal Models
• In-Context Learning
• Prompt Retrieval
• Computer Vision
• Qwen2.5-VL-7B
• GPT-4o

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.