Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation

TL;DR

This paper introduces SEED, a novel self-evolving distillation method that effectively identifies, isolates, and purges hallucinations in LVLMs, significantly improving their reliability without external tools.

Contribution

The paper proposes a self-evolving distillation framework with mode-seeking and hallucination elimination components to mitigate hallucinations in LVLMs, advancing beyond existing external-tool-based methods.

Findings

Substantial reduction in hallucinations across benchmarks

F1 score of LLaVA-1.5 improved from 81.3 to 88.3

Effective in models like LLaVA-1.5 and InternVL2

Abstract

Large Vision-Language Models (LVLMs) have demonstrated remarkable advancements in numerous areas such as multimedia. However, hallucination issues significantly limit their credibility and application potential. Existing mitigation methods typically rely on external tools or the comparison of multi-round inference, which significantly increase inference time. In this paper, we propose \textbf{SE}lf-\textbf{E}volving \textbf{D}istillation (\textbf{SEED}), which identifies hallucinations within the inner knowledge of LVLMs, isolates and purges them, and then distills the purified knowledge back into the model, enabling self-evolution. Furthermore, we identified that traditional distillation methods are prone to inducing void spaces in the output space of LVLMs. To address this issue, we propose a Mode-Seeking Evolving approach, which performs distillation to capture the dominant modes of the purified knowledge distribution, thereby avoiding the chaotic results that could emerge from void spaces. Moreover, we introduce a Hallucination Elimination Adapter, which corrects the dark knowledge of the original model by learning purified knowledge. Extensive experiments on multiple benchmarks validate the superiority of our SEED, demonstrating substantial improvements in mitigating hallucinations for representative LVLM models such as LLaVA-1.5 and InternVL2. Remarkably, the F1 score of LLaVA-1.5 on the hallucination evaluation metric POPE-Random improved from 81.3 to 88.3.