Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning

TL;DR

This paper introduces a new benchmark and a fine-tuning method to reduce hallucinations in large vision language models caused by adversarial dialogues, improving their reliability in multi-turn interactions.

Contribution

It presents an adversarial question generator for benchmarking and proposes adversarial instruction tuning to mitigate dialogue hallucinations in LVLMs.

Findings

Zero-shot performance drops significantly on the benchmark.

Bias toward preceding dialogues causes hallucinations.

Adversarial instruction tuning reduces hallucinations effectively.

Abstract

Mitigating hallucinations of Large Vision Language Models,(LVLMs) is crucial to enhance their reliability for general-purpose assistants. This paper shows that such hallucinations of LVLMs can be significantly exacerbated by preceding user-system dialogues. To precisely measure this, we first present an evaluation benchmark by extending popular multi-modal benchmark datasets with prepended hallucinatory dialogues powered by our novel Adversarial Question Generator (AQG), which can automatically generate image-related yet adversarial dialogues by adopting adversarial attacks on LVLMs. On our benchmark, the zero-shot performance of state-of-the-art LVLMs drops significantly for both the VQA and Captioning tasks. Next, we further reveal this hallucination is mainly due to the prediction bias toward preceding dialogues rather than visual content. To reduce this bias, we propose Adversarial Instruction Tuning (AIT) that robustly fine-tunes LVLMs against hallucinatory dialogues. Extensive experiments show our proposed approach successfully reduces dialogue hallucination while maintaining performance.