Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

arXiv:2404.00213v1 Announce Type: new
Abstract: In recent years, Large Language Models (LLMs) have shown remarkable performance in generating human-like text, proving to be a valuable asset across various applications. However, adapting these models to incorporate new, out-of-domain knowledge remains a challenge, particularly for facts and events that occur after the model's knowledge cutoff date. This paper investigates the effectiveness of Supervised Fine-Tuning (SFT) as a method for knowledge injection in LLMs, specifically focusing on the domain of recent sporting events. …

abstract applications arxiv challenge cs.cl domain domain knowledge events facts fine-tuning however human human-like knowledge language language models large language large language models llms performance supervised fine-tuning text type via