Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models

TL;DR

This paper introduces reflective instruction tuning with rationale learning to reduce hallucinations in large vision-language models, using a new dataset REVERIE to improve reasoning and alignment.

Contribution

It proposes a novel training method that incorporates rationale prediction, enhancing reasoning and reducing hallucinations in LVLMs, supported by a large annotated dataset REVERIE.

Findings

Improved performance on multiple LVLM benchmarks.

Enhanced reasoning capabilities in models.

Reduction in hallucination instances.

Abstract

Large vision-language models (LVLMs) have shown promising performance on a variety of vision-language tasks. However, they remain susceptible to hallucinations, generating outputs misaligned with visual content or instructions. While various mitigation strategies have been proposed, they often neglect a key contributor to hallucinations: lack of fine-grained reasoning supervision during training. Without intermediate reasoning steps, models may establish superficial shortcuts between instructions and responses, failing to internalize the inherent reasoning logic. To address this challenge, we propose reflective instruction tuning, which integrates rationale learning into visual instruction tuning. Unlike previous methods that learning from responses only, our approach entails the model predicting rationales justifying why responses are correct or incorrect. This fosters a deeper engagement with the fine-grained reasoning underlying each response, thus enhancing the model's reasoning proficiency. To facilitate this approach, we propose REVERIE, the first large-scale instruction-tuning dataset with ReflEctiVE RatIonalE annotations. REVERIE comprises 115k machine-generated reasoning instructions, each meticulously annotated with a corresponding pair of correct and confusing responses, alongside comprehensive rationales elucidating the justification behind the correctness or erroneousness of each response. Experimental results on multiple LVLM benchmarks reveal that reflective instruction tuning with the REVERIE dataset yields noticeable performance gain over the baseline model, demonstrating the effectiveness of reflecting from the rationales. Project page is at https://zjr2000.github.io/projects/reverie.