Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions (2411.09018v1)

Abstract: Recent research increasingly focuses on training vision-LLMs (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.

• The paper introduces Knowledge Adapted (KnowAda) fine-tuning, reducing hallucinations in small-to-medium VLMs by aligning training data complexity with existing model knowledge.
• The study employs a novel Decomposed NLI (DNLI) framework to evaluate captions by dissecting them into atomic propositions for granular measurement of descriptiveness and accuracy.
• Empirical results show KnowAda significantly decreases hallucination rates and improves descriptive precision, demonstrating a favorable trade-off between caption richness and accuracy in VLMs.

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

The paper "Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions" provides a significant contribution to the development of small to medium-scale Vision-LLMs (VLMs), focusing on optimizing fine-tuning methodologies to enhance their descriptive capabilities without escalating hallucinations. It presents a refined data-centric approach, coined as Knowledge Adapted (KnowAda) fine-tuning, dedicated to harnessing the latent capabilities of VLMs by aligning training data with the pre-existing knowledge ingrained within these models. This method specifically targets the reduction of content hallucinations that frequently plague VLMs when fine-tuned with overly complex or intricate image captions.

The paper critically assesses the interplay between descriptive richness and hallucination risks, deploying a novel Decomposed NLI (DNLI) evaluation framework that dissects generated captions into atomic propositions. Each proposition is assessed for factual entailment against the ground truth, providing a granular measurement of both descriptiveness and accuracy. Such an approach is particularly insightful for analyzing the fine-tuning dynamics of smaller multimodal models (up to 7B parameters), which are poised as vital for real-time applications but often falter in capturing intricate visual details.

The paper's core innovation, KnowAda, automatically adjusts the training dataset's complexity by aligning it with the model's extant knowledge. This automatic adaptation entails generating visual questions to probe the VLMs' knowledge gaps and subsequently tweaking the captions to excise the details that the model struggles to process correctly. By doing so, KnowAda balances detailed descriptiveness and factual accuracy more effectively than conventional data curation or caption simplification methods.

Empirically, the authors validate KnowAda by fine-tuning several VLMs across two distinct dense captioning datasets, demonstrating its efficacy through both automatic metrics and human evaluations. Notably, the results show a marked decrease in hallucination rates, suggesting that fine-tuning with KnowAda-adapted captions ensures a more stable and reliable descriptive output.

The paper suggests that KnowAda not only enhances descriptiveness precision but also improves hallucination precision and recall, presenting a favorable trade-off between descriptive richness and hallucination risks. The research underscores a salient point—training models on information they already possess strengthens their learning capabilities without overburdening them with new, potentially misaligned data.

One could foresee the potential enhancements in real-world applications, especially in aiding visually impaired individuals or augmenting automated descriptive systems, where the balance between richness of detail and accuracy is crucial. The prospects for further development in AI lie in extending the concept of knowledge adaptation to other modalities and tasks, like visual question answering, which demand equitable precision in understanding and generating language-based reasoning from visual inputs.

In conclusion, the paper offers a comprehensive analysis and methodology to improve small-to-medium scale VLMs' training efficiency by obviating hallucinations while upholding descriptiveness. This research opens avenues for further exploration into multimodal systems' optimization and adaptation, reinforcing the relevance of leveraging a model's pre-existing knowledge for more robust and reliable AI systems.