52 citations · 58 across the 3 of their papers we have counts for
3 papers
A Study of Optimizations for Fine-tuning Large Language Models
Arjun Singh, Nikhil Pandey, Anup Shirgaonkar +2
Fine-tuning large language models is a popular choice among users trying to adapt them for specific applications. However, fine-tuning these models is a demanding task because the…
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li +9
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,…
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha +13
There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented…