52 citations · 77 across the 6 of their papers we have counts for
6 papers
Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data
Anup Shirgaonkar, Nikhil Pandey, Nazmiye Ceren Abay +2
Leading open-source large language models (LLMs) such as Llama-3.1-Instruct-405B are extremely capable at generating text, answering questions, and solving a variety of natural lan…
MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging
Noel C. F. Codella, Ying Jin, Shrey Jain +28
In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…
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…
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
Bruno Silva, Leonardo Nunes, Roberto Estevão +2
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains, including healthcare and finance. For some tasks, L…