i-SRT: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective Judgment

• URL: http://arxiv.org/abs/2406.11280v1
• Date: Mon, 17 Jun 2024 07:33:30 GMT
• Title: i-SRT: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective Judgment
• Authors: Daechul Ahn, Yura Choi, San Kim, Youngjae Yu, Dongyeop Kang, Jonghyun Choi,
• Abstract summary: We propose a novel method that employs self-retrospection to enhance both response generation and preference modeling. Our empirical evaluations across diverse video question answering benchmarks demonstrate that i-SRT significantly outperforms prior arts.
• Score: 36.69910114305134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aligning Video Large Multimodal Models (VLMMs) face challenges such as modality misalignment and verbose responses. Although iterative approaches such as self-rewarding or iterative direct preference optimization (DPO) recently showed a significant improvement in language model alignment, particularly on reasoning tasks, self-aligned models applied to large video-language models often result in lengthy and irrelevant responses. To address these challenges, we propose a novel method that employs self-retrospection to enhance both response generation and preference modeling, and call iterative self-retrospective judgment (i-SRT). By revisiting and evaluating already generated content and preference in loop, i-SRT improves the alignment between textual and visual modalities, reduce verbosity, and enhances content relevance. Our empirical evaluations across diverse video question answering benchmarks demonstrate that i-SRT significantly outperforms prior arts. We are committed to opensourcing our code, models, and datasets to encourage further investigation.

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