Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation

TL;DR

This paper introduces DualityForge, a diffusion-based video editing framework that generates counterfactual videos and QA pairs to reduce hallucinations in Multimodal Large Language Models, improving their robustness and accuracy.

Contribution

The paper presents a novel counterfactual data synthesis method and a specialized training regime to significantly decrease hallucinations in MLLMs during video understanding tasks.

Findings

24.0% reduction in hallucinations on counterfactual videos

Improved performance on hallucination and general benchmarks

Effective contrastive training with synthesized data

Abstract

Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an over-reliance on language priors, which can lead to visual ungrounded hallucinations, especially when processing counterfactual videos that defy common sense. This limitation, stemming from the intrinsic data imbalance between text and video, is challenging to address due to the substantial cost of collecting and annotating counterfactual data. To address this, we introduce DualityForge, a novel counterfactual data synthesis framework that employs controllable, diffusion-based video editing to transform real-world videos into counterfactual scenarios. By embedding structured contextual information into the video editing and QA generation processes, the framework automatically produces high-quality QA pairs together with original-edited video pairs for contrastive training. Based on this, we build DualityVidQA, a large-scale video dataset designed to reduce MLLM hallucinations. In addition, to fully exploit the contrastive nature of our paired data, we propose Duality-Normalized Advantage Training (DNA-Train), a two-stage SFT-RL training regime where the RL phase applies pair-wise $\ell_1$ advantage normalization, thereby enabling a more stable and efficient policy optimization. Experiments on DualityVidQA-Test demonstrate that our method substantially reduces model hallucinations on counterfactual videos, yielding a relative improvement of 24.0% over the Qwen2.5-VL-7B baseline. Moreover, our approach achieves significant gains across both hallucination and general-purpose benchmarks, indicating strong generalization capability. We will open-source our dataset and code.