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

2024/08/04 by Muralidharan, Balaji, Beadles, Hayden, Marzban, Reza +1 · 1 citation
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2408.04651

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.

Cited by

• FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs

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