Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding
<|reference_start|>Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific Knowledge Extraction and Understanding: 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.<|reference_end|>
@article{muralidharan2024knowledge,
    title={Knowledge AI: Fine-tuning NLP Models for Facilitating Scientific
  Knowledge Extraction and Understanding},
    author={Balaji Muralidharan, Hayden Beadles, Reza Marzban, Kalyan Sashank
  Mupparaju},
    journal={arXiv preprint arXiv:2408.04651},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04651},
    primaryClass={cs.CL cs.AI}
}