On Domain-Specific Post-Training for Multimodal Large Language Models

Recent years have witnessed the rapid development of general multimodal large language models (MLLMs). However, adapting general MLLMs to specific domains, such as scientific fields and industrial applications, remains less explored. This paper systematically investigates domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. (1) Data Synthesis: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs. (2) Training Pipeline: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. (3) Task Evaluation: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks. To support further research in MLLM domain adaptation, we will open-source our implementations.

Host: Hey everyone, and welcome back to the podcast! Today, we're diving into something really exciting: the world of multimodal large language models, and specifically, how we can make them even better by adapting them to specific domains. Think about it – a model that's amazing at general tasks, but then becomes a true expert in, say, medical imaging or culinary arts. That's what we're talking about.

Guest: Exactly, Leo. It's a fascinating area. General-purpose MLLMs are impressive, but their performance drops significantly when you move away from the broad data they were trained on. The real power comes from fine-tuning them for specific applications, and that's what makes this research so important.

Host: Absolutely. So, let's talk about the approach taken in this paper. They've broken it down into several key parts: introduction, related work, their method, experimental setup, results, ablation studies, analysis, and conclusions. It's a pretty comprehensive approach.

Guest: Yeah, that's a solid structure for a research paper. The introduction sets the stage perfectly, highlighting the limitations of general MLLMs in specialized domains. They point out the challenges in scientific fields and industrial applications, where you've got specialized images and terminology, or maybe even privacy restrictions limiting the data available for training.

Host: Exactly. And the 'Related Work' section is crucial; it shows they've done their homework and understand the existing landscape. They categorize previous attempts at domain-specific data synthesis and training, highlighting the use of manual rules, closed-source models (which raise privacy concerns!), and the common two-stage training pipelines. They then explain how their work improves upon these existing methods.

Guest: Their method is where things get really interesting. The core idea is a visual instruction synthesizer. Instead of manually creating visual instruction tasks, which is incredibly time-consuming and requires expertise, they use an open-source model to generate these tasks from image-caption pairs. This is a clever way to leverage readily available data and reduce the reliance on human experts.

Host: That's brilliant! And to enhance accuracy, they've implemented a consistency-based filter. They don't just blindly trust the synthesizer; they have another model check the consistency of the generated instructions and responses. This clever filtering step helps weed out the inaccurate stuff before training.

Guest: Precisely. And their training pipeline is a nice simplification. Instead of the typical two-stage approach, they opt for a single-stage process, combining image-caption pairs with the synthesized visual instructions. This is important because it avoids potential issues with catastrophic forgetting – where the model forgets what it learned in the first stage during the second stage. It keeps the training more unified.

Host: Right, that makes total sense. They tested this on various models, including Qwen2-VL-2B, LLaVA-v1.6-8B, and Llama-3.2-11B, across two domains: biomedicine and food. The results seem pretty compelling, showing consistent improvement over general-purpose MLLMs.

Guest: The ablation studies are important too. They systematically remove different components of their method to see how each one contributes. That helps isolate the impact of their visual instruction synthesizer, the consistency-based filter, and the single-stage training approach. They're not just saying it works; they're showing why it works.

Host: And their analysis goes deeper still, examining the quality of the synthesized data and the effect of different choices in their synthesis process. They even have some visualizations to show the diversity of the generated tasks and some example tasks that demonstrate the superiority of their method over manual rules and GPT-4/GPT-4V generated tasks.

Guest: The paper concludes by highlighting the key contributions and emphasizes the potential for broader adoption of their methods. They've open-sourced their code, which is fantastic for the research community. It allows others to build on their work and accelerate progress in this area.