EventHallusion: Diagnosing Event Hallucinations in Video LLMs

2024-09-25

arXiv.org

AI Summary

Video large language models (LLMs) suffer from severe hallucinations in event understanding, exacerbated by linguistic priors and vision-language misalignment. To address this, we introduce EventHallusion—the first benchmark dedicated to diagnosing event-level hallucinations in video LLMs—systematically defining, quantifying, and attributing such hallucinations from dual perspectives: linguistic priors and cross-modal biases. We propose temporal contrastive decoding (TCD), a novel, fine-tuning-free inference-time method that explicitly models temporal cues to suppress hallucination. Leveraging event-driven adversarial video construction and a multidimensional evaluation framework, we validate TCD across eight open-source and two closed-source models. Results show that TCD significantly enhances the reliability of event understanding, improving accuracy by over 15% for several models. This work establishes a new paradigm for trustworthy evaluation and robust reasoning in video LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recently, Multimodal Large Language Models (MLLMs) have made significant progress in the video comprehension field. Despite remarkable content reasoning and instruction following capabilities they demonstrated, the hallucination problem of these VideoLLMs is less explored compared with its counterpart in the image domain. To mitigate this gap, we propose EventHallusion, a novel benchmark that focuses on assessing the VideoLLMs' hallucination toward event, the crux of video analysis. From a hallucination attribution perspective, our EventHallusion benchmark is curated to assess a VideoLLM's susceptibility toward language priors and vision-language biases. On the other hand, we also propose a simple yet effective method, called Temporal Contrastive Decoding (TCD), to tackle the hallucination problems of VideoLLMs. The proposed TCD method rectifies the model's bias toward its priors during the decoding stage by comparing the original video with a modified version, in which temporal cues are disrupted. Through comprehensive evaluation of eight open-source and two closed-source VideoLLMs on the proposed EventHallusion benchmark, we observe that the open-source models suffer significantly from hallucination problems, whereas the closed-source ones perform markedly better. By further equipping open-source VideoLLMs with the proposed TCD approach, evident performance improvements are achieved across most metrics in the EventHallusion benchmark. Our codes and benchmark data are available at https://github.com/Stevetich/EventHallusion.

Problem

• Multimodal Large Language Models
• Visual Content Understanding
• Bias in Language and Vision

Innovation

• EventHallusion
• Time Contrastive Decoding (TCD)
• Multimodal Language Model