Knowledge AI：微调 NLP 模型以促进科学知识抽取与理解
Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
AI总结：
该研究提出 Knowledge AI 框架，通过科学领域数据微调 LLM，以提升摘要、生成、问答和命名实体识别能力，支持非专业人员进行科学知识查询与发现。
摘要：
本项目研究 Large Language Models（LLMs，大语言模型）在理解和抽取特定领域科学知识方面的效能，并创建一个深度学习框架：Knowledge AI。作为该框架的一部分，我们采用预训练模型，并在科学领域的数据集上对其进行微调。这些模型被适配用于四项关键 Natural Language Processing（NLP，自然语言处理）任务：摘要、文本生成、问答和命名实体识别。结果表明，面向特定领域的微调显著提升了模型在上述每项任务中的性能，从而增强了它们在科学情境中的适用性。这种适配使非专业人员能够高效查询并抽取目标科学领域内的信息，展示了经过微调的 LLMs 作为科学知识发现工具的潜力。
英文摘要：
This project investigates the efficacy of Large Language Models (LLMs) in understanding and extracting scientific knowledge across specific domains and to create a deep learning framework: Knowledge AI. As a part of this framework, we employ pre-trained models and fine-tune them on datasets in the scientific domain. The models are adapted for four key Natural Language Processing (NLP) tasks: summarization, text generation, question answering, and named entity recognition. Our results indicate that domain-specific fine-tuning significantly enhances model performance in each of these tasks, thereby improving their applicability for scientific contexts. This adaptation enables non-experts to efficiently query and extract information within targeted scientific fields, demonstrating the potential of fine-tuned LLMs as a tool for knowledge discovery in the sciences.
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。
