Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs

TL;DR

This paper introduces an adversarial parametric editing framework called ALEAHallu to reduce hallucinations in vision-language models by fine-tuning critical parameters with adversarial prompts that emphasize visual evidence.

Contribution

The paper presents a novel Activate-Locate-Edit adversarial paradigm for targeted parameter fine-tuning to mitigate hallucinations in VLMs, surpassing heuristic calibration methods.

Findings

Significant reduction in hallucinations across tasks

Effective identification of hallucination-prone parameter clusters

Improved alignment with visual inputs

Abstract

While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs' over-reliance on linguistic priors and insufficient visual feature integration, proposing heuristic decoding calibration strategies to mitigate them. However, the non-trainable nature of these strategies inherently limits their optimization potential. To this end, we propose an adversarial parametric editing framework for Hallucination mitigation in VLMs, which follows an \textbf{A}ctivate-\textbf{L}ocate-\textbf{E}dit \textbf{A}dversarially paradigm. Specifically, we first construct an activation dataset that comprises grounded responses (positive samples attentively anchored in visual features) and hallucinatory responses (negative samples reflecting LLM prior bias and internal knowledge artifacts). Next, we identify critical hallucination-prone parameter clusters by analyzing differential hidden states of response pairs. Then, these clusters are fine-tuned using prompts injected with adversarial tuned prefixes that are optimized to maximize visual neglect, thereby forcing the model to prioritize visual evidence over inherent parametric biases. Evaluations on both generative and discriminative VLM tasks demonstrate the significant effectiveness of ALEAHallu in alleviating hallucinations. Our code is available at https://github.com/hujiayu1223/ALEAHallu.