Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions

TL;DR

This paper introduces KnowAda, a knowledge-adapted fine-tuning method for vision-language models that reduces hallucinations in detailed captions while maintaining descriptiveness, validated across multiple small-scale models and datasets.

Contribution

We propose KnowAda, a novel data-centric fine-tuning approach that automatically adapts training data to balance caption richness and hallucination prevention in VLMs.

Findings

KnowAda reduces hallucinations effectively.

It maintains high descriptive quality.

Outperforms baseline methods in evaluations.

Abstract

Recent research increasingly focuses on training vision-language models (VLMs) with long, detailed image captions. However, small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-tuning. In this paper, we explore how well VLMs adapt to such captions. To quantify caption quality, we propose Decomposed NLI (DNLI), an evaluation framework that breaks down generated captions into individual propositions, assessing each in isolation. This fine-grained analysis reveals a critical balance between capturing descriptive details and preventing hallucinations. Our findings show that simply reducing caption complexity or employing standard data curation techniques does not effectively resolve this issue. To tackle this challenge, we introduce Knowledge Adapted (KnowAda) fine-tuning, a data-centric approach that automatically adapts training data with the model's existing knowledge and visual understanding. KnowAda minimizes hallucinations while preserving high descriptiveness. We validate this approach across several small-scale VLMs (up to 7B parameters) and dense caption datasets, demonstrating that KnowAda effectively balances hallucination reduction and descriptiveness. Our results show that KnowAda outperforms various baselines in both automatic metrics and human evaluations. We will release our code and models.