April 2, 2024, 7:51 p.m. | Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li, Daniel Holstein, Leonardo Nunes, Sara Malvar, Bruno Silva, Ranveer Chandra, Vijay Aski, Pavan Kumar Reddy Ya

cs.CL updates on arXiv.org arxiv.org

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

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