Through additional training, we explore embedding specialized scientific knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that effective knowledge integration requires reading texts from multiple perspectives, especially in instructional formats. We utilize text augmentation to tackle the scarcity of specialized texts, including style conversions and translations. Hyperparameter optimization proves crucial, with different size models (7b, 13b, and 70b) reasonably undergoing additional training. Validating our methods, we construct a dataset of 65,000 scientific papers. Although we have succeeded in partially embedding knowledge, the study highlights the complexities and limitations of incorporating specialized information into LLMs, suggesting areas for further improvement.
@article{arxiv.2312.03360,
title = {Teaching Specific Scientific Knowledge into Large Language Models through Additional Training},
author = {Kan Hatakeyama-Sato and Yasuhiko Igarashi and Shun Katakami and Yuta Nabae and Teruaki Hayakawa},
journal= {arXiv preprint arXiv:2312.03360},
year = {2023}
}
Comments
added token information for some texts, and fixed typo