Sharifymoghaddam, S., Upadhyay, S., Chen, W., & Lin, J. (2024). UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models. arXiv preprint arXiv:2405.10311v2.
This research paper introduces UniRAG, a plug-and-play technique that integrates retrieval augmentation with MM-LLMs to enhance their generation quality in multi-modal tasks, particularly image captioning and image generation.
UniRAG employs a two-stage retrieval and generation workflow. In the retrieval stage, UniIR models (CLIP Score Fusion and BLIP Feature Fusion) retrieve relevant image-text pairs from a large multi-modal database based on the input query (image or caption). In the generation stage, these retrieved pairs are incorporated as few-shot examples into the prompts of various MM-LLMs (LLaVA, Gemini-Pro, GPT-4o for captioning; LaVIT, Emu2-Gen for image generation) to guide their generation process.
UniRAG offers a model-agnostic approach to enhance the fidelity of MM-LLM outputs by leveraging retrieval augmentation. The technique effectively addresses the limitations of MM-LLMs in handling lesser-known entities or uncommon combinations of common ones, leading to more accurate and contextually relevant generations.
This research contributes to the advancement of MM-LLMs by introducing a practical and effective method for improving their generation quality. UniRAG's plug-and-play nature makes it easily adaptable to various MM-LLMs and multi-modal tasks, potentially impacting applications that require high-fidelity multi-modal understanding and generation.
The study primarily focuses on English-only datasets and models. Further research is needed to explore UniRAG's effectiveness in multilingual settings and with low-resource languages. Additionally, investigating the generalizability of retriever-guided generation to out-of-domain retrieval scenarios and incorporating factors beyond relevance for responsible AI deployment are crucial areas for future exploration.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Sahel Sharif... at arxiv.org 10-22-2024
https://arxiv.org/pdf/2405.10311.pdfDeeper Inquiries