The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio

TL;DR

This paper systematically investigates hallucinations in large multimodal models across language, visual, and audio modalities, revealing key vulnerabilities and proposing a benchmark to evaluate and address these issues.

Contribution

It introduces the first comprehensive benchmark for evaluating hallucinations in multimodal models and analyzes the main causes of these hallucinations.

Findings

Hallucinations stem from overreliance on unimodal priors.

Spurious inter-modality correlations contribute to hallucinations.

Imbalances in modality integration lead to increased hallucination risk.

Abstract

Recent advancements in large multimodal models (LMMs) have significantly enhanced performance across diverse tasks, with ongoing efforts to further integrate additional modalities such as video and audio. However, most existing LMMs remain vulnerable to hallucinations, the discrepancy between the factual multimodal input and the generated textual output, which has limited their applicability in various real-world scenarios. This paper presents the first systematic investigation of hallucinations in LMMs involving the three most common modalities: language, visual, and audio. Our study reveals two key contributors to hallucinations: overreliance on unimodal priors and spurious inter-modality correlations. To address these challenges, we introduce the benchmark The Curse of Multi-Modalities (CMM), which comprehensively evaluates hallucinations in LMMs, providing a detailed analysis of their underlying issues. Our findings highlight key vulnerabilities, including imbalances in modality integration and biases from training data, underscoring the need for balanced cross-modal learning and enhanced hallucination mitigation strategies. Based on our observations and findings, we suggest potential research directions that could enhance the reliability of LMMs.