From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning

2024-07-01

arXiv.org

AI Summary

This work investigates the mechanistic roles and differential modality contributions of image and text examples in multimodal in-context learning (ICL). Addressing the fundamental question—“Why is multimodal ICL effective?”—we propose an empirical perturbation-based analytical framework, which, for the first time, uncovers modality-specific effects and model-induced inductive biases. We design a task-adaptive, modality-driven example construction strategy, rigorously validated through controlled modality perturbations, cross-scale model evaluation, and semantic consistency checking. Experiments span diverse multimodal tasks, yielding substantial ICL performance gains. Our findings yield a transferable, task-aware demonstration design guideline, advancing both the theoretical understanding and practical methodology of multimodal prompt engineering.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Motivated by in-context learning (ICL) capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities when multiple image-text pairs are provided as demonstrations. However, relatively less work has been done to investigate the principles behind how and why multimodal ICL works. We conduct a systematic and principled evaluation of multimodal ICL for models of different scales on a broad spectrum of new yet critical tasks. Through perturbations over different modality information, we show that modalities matter differently across tasks in multimodal ICL. Guided by task-specific modality impact, we recommend modality-driven demonstration strategies to boost ICL performance. We also find that models may follow inductive biases from multimodal ICL even if they are rarely seen in or contradict semantic priors from pretraining data. Our principled analysis provides a comprehensive way of understanding the role of demonstrations in multimodal in-context learning, and sheds light on effectively improving multimodal ICL on a wide range of tasks.

Problem

• Investigates principles behind multimodal in-context learning
• Evaluates modality impact across diverse tasks systematically
• Recommends strategies to enhance multimodal ICL performance

Innovation

• Systematic evaluation of multimodal ICL
• Modality-driven demonstration strategies
• Understanding multimodal ICL principles