A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks
It addresses the gap in understanding LLM capabilities for biomedical text processing, offering insights for researchers in computational biology and healthcare, but it is incremental as it focuses on benchmarking rather than introducing new methods.
This paper evaluated the performance of four large language models (LLMs) on six biomedical tasks across 26 datasets, finding that zero-shot LLMs can outperform fine-tuned biomedical models on datasets with smaller training sets, though they underperform on tasks with large annotated data.
Recently, Large Language Models (LLM) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, we conduct a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art fine-tuned biomedical models. This suggests that pretraining on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.