Pareto-Based Preference Alignment for Reducing Hallucinations in Multi-Turn Multimodal Dialogue on MMHal-Bench

Abstract

Abstract: Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challenged by hallucinations, which can be attributed to the modality misalignment and the inherent hallucinations of their underlying Large Language Models (LLMs) backbone. Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment, resulting in over-reliance on LLMs and hallucinations. In this paper, we propose Entity-centric Multimodal Preference Optimization (EMPO), wh

Research Question

How does Pareto-based preference alignment affect hallucination rates in multi-turn multimodal dialogue compared to standard RLHF on the MMHal-Bench dataset?

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