探索指令微调在生物医学语言处理中的有效性
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing
AI总结：
本研究探讨指令微调在生物医学NLP中的潜力，通过构建约20万样本的数据集训练模型，在NER、RE和NLI等任务上取得与专用编码器模型相媲美的效果。
摘要：
大型语言模型（LLMs），特别是类似ChatGPT的模型，已显著影响了自然语言处理（NLP）领域。尽管这些模型在通用语言任务中表现出色，但它们在特定领域的下游任务中的性能仍在发展中，例如生物医学和临床命名实体识别（NER）、关系抽取（RE）以及医学自然语言推理（NLI）。在此背景下，我们的研究调查了指令微调在生物医学语言处理中的潜力，将此技术应用于两个具有相当规模的通用LLMs。我们提出了一个全面的基于指令的模型，该模型在一个包含约$200,000$个以指令为中心的样本的数据集上进行训练。该数据集代表了对现有数据的精心策划汇编，经过细致的改编和重新格式化，以符合我们基于指令任务的特定要求。这一举措代表了在利用此类模型方面迈出的重要一步，旨在在各类经典生物医学NLP任务中取得与BioBERT和BioClinicalBERT等专用纯编码器模型相媲美的结果。我们的工作包括分析数据集的组成及其对模型性能的影响，从而提供对指令微调复杂性的见解。通过分享我们的代码、模型以及独特组装的基于指令的数据集，我们旨在鼓励该领域持续的研究与开发。
英文摘要：
Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in general language tasks, their performance in domain-specific downstream tasks such as biomedical and clinical Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI) is still evolving. In this context, our study investigates the potential of instruction tuning for biomedical language processing, applying this technique to two general LLMs of substantial scale. We present a comprehensive, instruction-based model trained on a dataset that consists of approximately $200,000$ instruction-focused samples. This dataset represents a carefully curated compilation of existing data, meticulously adapted and reformatted to align with the specific requirements of our instruction-based tasks. This initiative represents an important step in utilising such models to achieve results on par with specialised encoder-only models like BioBERT and BioClinicalBERT for various classical biomedical NLP tasks. Our work includes an analysis of the dataset's composition and its impact on model performance, providing insights into the intricacies of instruction tuning. By sharing our codes, models, and the distinctively assembled instruction-based dataset, we seek to encourage ongoing research and development in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
