Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

2026-09-08

AI Summary

为解决视觉-语言模型中语义与实用性差距问题，提出一种两阶段生成器循环对齐框架，通过生成器引导信号优化重排序器，提高答案准确性。

Abstract

Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers and rerankers optimize for semantic similarity rather than answer utility, creating a preference gap: documents that appear relevant may not help the generator produce a correct answer. Motivated by this, we propose a two-stage generator-in-the-loop alignment framework that closes this gap without human document-level relevance annotations. Our framework consists of two stages: in Stage 1, a VLM generates a hypothetical text passage from the image-query pair, which is used as the retrieval query for dense text search, bridging the image-to-text modality gap. In Stage 2, a cross-encoder reranker adapted with low-rank adaptation (LoRA) is fine-tuned using answer-supervised preference pairs mined from the frozen VLM: given the dataset answer label, a candidate document is labeled positive if the VLM produces the correct answer when given that document as context, and negative otherwise. This generator-guided signal is compatible with multiple alignment loss functions, including contrastive (triplet) loss, pairwise direct preference optimization (DPO), and supervised fine-tuning (SFT), and supports periodic re-mining to refresh preference pairs as the reranker improves. Experiments on VQA-X and A-OKVQA with Qwen3.5-2B and Qwen3-VL-4B-Instruct show that our proposed framework consistently outperforms rank-order, random, and REPLUG-style likelihood baselines under various alignment losses and pool size settings, suggesting that answer-level generator feedback is an effective supervision signal for preference alignment.

Problem

• Semantic-Utility Gap
• Multimodal RAG
• Generator-in-the-Loop
• Answer Utility
• Retrieval-Augmented Generation

Innovation

• generator-in-the-loop
• alignment framework
• cross-encoder reranker
• low-rank adaptation (LoRA)
• answer-level feedback