GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models

• URL: http://arxiv.org/abs/2606.12744v1
• Date: Wed, 10 Jun 2026 23:14:45 GMT
• Title: GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
• Abstract summary: In-context learning has become a powerful mechanism for adapting Large Language Models to new tasks without fine-tuning.<n>We propose Guided Retrieval of In-context Prompts (GRIP), a learnable vision-only retrieval framework.<n>GRIP learns to distinguish beneficial from detrimental in-context examples through contrastive training.
• Score: 42.55431877219008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Models (LMMs), Multimodal In-Context Learning (M-ICL) relies on retrieving relevant examples, such as images, captions, or question-answer pairs, to guide predictions across tasks like classification, captioning, and visual question answering (VQA). Most existing approaches select in-context examples based on feature-space similarity, assuming that semantically similar samples provide the most useful context. However, our systematic analysis reveals that this assumption does not always hold: visually similar examples are not necessarily those that most effectively enhance in-context learning performance. To address this, we propose the Guided Retrieval of In-context Prompts (GRIP), a learnable vision-only retrieval framework that leverages feedback from LMMs to identify examples that truly improve model predictions. GRIP learns to distinguish beneficial from detrimental in-context examples through contrastive training, refining retrieval beyond pure similarity. Across three multimodal tasks, namely classification, captioning, and VQA, GRIP improves consistently over similarity-based retrieval on Qwen2.5-VL-7B, with its strongest gains in classification on Idefics2-8B. Moreover, we demonstrate that retrievers trained with feedback from one open LMM can be transferred to other models without retraining, including closed-source GPT-4o and Gemini, enabling scalable and cost-efficient deployment of M-ICL. Code will be published upon acceptance.

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