Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding

• URL: http://arxiv.org/abs/2408.04651v1
• Date: Sun, 4 Aug 2024 01:32:09 GMT
• Title: Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
• Authors: Balaji Muralidharan, Hayden Beadles, Reza Marzban, Kalyan Sashank Mupparaju,
• Abstract summary: This project investigates the efficacy of Large Language Models (LLMs) in understanding and extracting scientific knowledge across specific domains. We employ pre-trained models and fine-tune them on datasets in the scientific domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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