Evaluating the Effectiveness of Cost-Efficient Large Language Models in Benchmark Biomedical Tasks
This work provides insights for selecting models in biomedical applications, but it is incremental as it focuses on evaluating existing models rather than introducing new methods.
The paper evaluated cost-efficient large language models on various biomedical tasks, finding no single model consistently outperformed others, with open-source models sometimes matching or exceeding closed-source ones in performance while offering faster inference and better privacy.
This paper presents a comprehensive evaluation of cost-efficient Large Language Models (LLMs) for diverse biomedical tasks spanning both text and image modalities. We evaluated a range of closed-source and open-source LLMs on tasks such as biomedical text classification and generation, question answering, and multimodal image processing. Our experimental findings indicate that there is no single LLM that can consistently outperform others across all tasks. Instead, different LLMs excel in different tasks. While some closed-source LLMs demonstrate strong performance on specific tasks, their open-source counterparts achieve comparable results (sometimes even better), with additional benefits like faster inference and enhanced privacy. Our experimental results offer valuable insights for selecting models that are optimally suited for specific biomedical applications.