On Domain-Adaptive Post-Training for Multimodal Large Language Models (2411.19930v3)

Abstract: Adapting general multimodal LLMs (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) Data Synthesis: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) Training Pipeline: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) Task Evaluation: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area.

• The paper introduces a novel single-stage training pipeline that adapts MLLMs to specialized domains.
• It leverages open-source visual instruction synthesis to generate diverse, domain-specific tasks from image-caption data.
• Experiments in biomedicine and food reveal significant performance gains over traditional two-stage training approaches.

Domain-Specific Adaptation in Multimodal LLMs

The paper "On Domain-Specific Post-Training for Multimodal LLMs" addresses the crucial task of adapting general-purpose Multimodal LLMs (MLLMs) to specialized domains, such as biomedicine and food. This research explores the often overlooked phase of domain-specific post-training, providing valuable insights into data synthesis, training methodologies, and task evaluation to enhance domain-specific performance in MLLMs.

Key Contributions

Visual Instruction Synthesis

The paper introduces a novel approach for visual instruction synthesis that leverages open-source models to generate diverse domain-specific tasks. A visually informative synthesizer is proposed, capable of creating instruction-response pairs from domain-specific image-caption data. This methodology contrasts with traditional rule-based and closed-source approaches by utilizing open-source tools to improve task diversity and domain knowledge infusion.

Single-Stage Training Pipeline

The research questions the conventional two-stage training paradigm commonly used in MLLMs. Instead, it advocates for a single-stage training pipeline that integrates synthetic visual instruction tasks with image-caption tasks. This approach is postulated to enhance task diversity, fostering MLLMs' generalization across various domain-specific tasks by preventing potential pitfalls such as catastrophic forgetting, which occurs in two-stage models.

Results and Analysis

The paper presents a comprehensive set of experiments conducted in the domains of biomedicine and food, utilizing renowned models such as Qwen2-VL-2B, LLaVA-v1.6-8B, and Llama-3.2-11B. The adapted models consistently outperform general models and competitive baselines on domain-specific tasks such as medical question answering (SLAKE, PathVQA) and food recipe generation (Recipe1M), highlighting the efficacy of the proposed methods.

Notably, the paper reveals that the synthesized tasks outperform those created by manual rules, GPT-4, and GPT-4V in improving performance on domain-specific tasks. Moreover, the single-stage training framework shows significant improvements over the traditional two-stage process, affirming the research’s hypothesis on preventing knowledge attrition during training transitions.

Implications and Future Directions

This work has practical implications for building domain-specific MLLMs that maintain robust performance across specialized tasks. By open-sourcing the proposed methodologies and implementations, this research provides a valuable resource for further investigations into domain adaptation, potentially extending to other domains beyond biomedicine and food. Moreover, the insights into synthetic data generation and training pipelines contribute to the broader understanding of enhancing LLM performance in specialized settings.

Future research could explore extending these methodologies to additional domains or integrating with real-world applications, such as developing medical diagnosis assistance tools or creating culinary AI systems. Additionally, improving accuracy in task synthesis without sacrificing task complexity remains a challenging yet promising avenue for subsequent exploration.

In conclusion, this paper successfully elucidates a pathway to enhance domain-specific performance in MLLMs through strategic post-training methodologies. The integration of diverse, domain-infused synthetic tasks coupled with an innovative training pipeline sets a foundation for further advancements in the field of domain-adaptive multimodal AI systems.