New framework ERIM enhances multimodal in-context learning by inducing rules

Researchers have introduced ERIM, a novel framework designed to improve multimodal in-context learning (MICL) by explicitly inducing the underlying rules from demonstration samples. Unlike existing methods that focus on direct input-output mapping, ERIM leverages the reasoning capabilities of multimodal large language models (MLLMs) to construct rule-centric datasets and provide explicit supervision for rule induction. This two-stage training strategy, combining supervised fine-tuning with reinforcement learning, has demonstrated consistent performance gains across various MICL tasks, leading to stronger and more stable results. AI

IMPACT Enhances the interpretability and performance of multimodal models by explicitly teaching them to reason over rules.

RANK_REASON The cluster contains a research paper detailing a new framework for multimodal in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• ERIM
• Gotit.pub
• Hugging Face
• Influence Flower
• Litmaps
• Multimodal In-Context Learning
• multimodal large language model
• ScienceCast
• scite Smart Citations

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →