X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation

2024-08-27

arXiv.org

AI Summary

Existing recommender systems often rely on unimodal prompts or coarse-grained multimodal fusion, failing to capture fine-grained complementary, supportive, and conflicting relationships between images and text—thereby limiting item representation richness and recommendation accuracy. To address this, we propose X-Reflect, the first cross-modal reflective prompting framework that explicitly guides large language models (LLMs) and multimodal large models (LMMs) to identify and reconcile textual-visual consistency and inconsistency. X-Reflect integrates three core mechanisms: contrastive reflection, modality alignment, and conflict awareness, enabling fine-grained, synergistic cross-modal understanding. Extensive experiments demonstrate that X-Reflect significantly outperforms both text-only and baseline multimodal prompting methods on two major benchmarks. Furthermore, it exhibits strong generalizability across diverse LMM backbones and robustness to prompt variations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) and Large Multimodal Models (LMMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or employ basic multimodal strategies that do not fully exploit the complementary information available from both textual and visual modalities. This paper introduces a novel framework, Cross-Reflection Prompting, termed X-Reflect, designed to address these limitations by prompting LMMs to explicitly identify and reconcile supportive and conflicting information between text and images. By capturing nuanced insights from both modalities, this approach generates more comprehensive and contextually richer item representations. Extensive experiments conducted on two widely used benchmarks demonstrate that our method outperforms existing prompting baselines in downstream recommendation accuracy. Additionally, we evaluate the generalizability of our framework across different LMM backbones and the robustness of the prompting strategies, offering insights for optimization. This work underscores the importance of integrating multimodal information and presents a novel solution for improving item understanding in multimodal recommendation systems.

Problem

• Improving multimodal recommendation accuracy by integrating text and visual information
• Addressing limitations of text-only or basic multimodal prompting strategies
• Generating comprehensive item representations through cross-modal reflection

Innovation

• Cross-Reflection Prompting reconciles text-image supportive and conflicting information
• Lightweight X-Reflect-keyword variant reduces input length by 50%
• Selective multimodal prompting based on text-image dissimilarity relationship