Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
This work addresses the challenge for non-experts in efficiently querying and extracting information from scientific domains, though it is incremental as it applies existing fine-tuning methods to new data.
The project tackled the problem of using Large Language Models (LLMs) for scientific knowledge extraction by fine-tuning them on domain-specific datasets, resulting in significant performance enhancements in summarization, text generation, question answering, and named entity recognition tasks.
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.