Alternative Solutions for Evaluating VQA Models: A Comprehensive Review

In this article, the authors aim to introduce a novel approach for solving medical visual question answering (Med-VQA) tasks using large language models (LLMs). They propose a two-stage fine-tuning strategy, where the pre-trained LLMs are first adapted for general domain and then fine-tuned on Med-VQA datasets. The authors highlight the importance of treating VQA tasks as generative tasks and demonstrate the effectiveness of their proposed approach by achieving state-of-the-art performance on several benchmark datasets.
To begin with, the authors explain that Med-VQA is a challenging task due to the complexity of medical images and the need for accurate annotations. They then introduce their proposed approach, which involves adapting pre-trained LLMs for general domain and fine-tuning them on Med-VQA datasets. The authors stress the significance of treating VQA tasks as generative tasks, where the goal is to generate accurate answers rather than simply classifying images.
The authors then delve into the specifics of their proposed approach, which involves two stages of fine-tuning. In the first stage, the pre-trained LLMs are adapted for general domain using a variety of techniques, such as adding domain-specific embeddings and modifying the model’s architecture. In the second stage, the adapted models are fine-tuned on Med-VQA datasets using various prompting techniques, such as adding medical-related context to the questions.
The authors also discuss the importance of evaluating the performance of their proposed approach in a fair and reliable manner. They suggest using metrics that take into account the diversity of the answers and the complexity of the tasks, rather than simply relying on accuracy scores.
Finally, the authors highlight some of the limitations of their proposed approach and suggest directions for future research. They note that there is still much to be explored in terms of improving the performance of Med-VQA models and expanding their capabilities to handle more complex tasks.
In summary, this article presents a novel approach for solving Med-VQA tasks using large language models. The proposed approach involves adapting pre-trained LLMs for general domain and fine-tuning them on Med-VQA datasets, treating VQA tasks as generative tasks, and evaluating performance in a fair and reliable manner. The authors demonstrate the effectiveness of their proposed approach by achieving state-of-the-art performance on several benchmark datasets.