Search Results for author:
Found 3 papers, 0 papers with code
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
We present a novel dataset generation process that leads to more effective knowledge ingestion through SFT, and our results show considerable performance improvements in Q&A tasks related to out-of-domain knowledge.
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results.
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
Our analysis highlights GPT-4's ability to achieve a passing score on exams to earn credits for renewing agronomist certifications, answering 93% of the questions correctly and outperforming earlier general-purpose models, which achieved 88% accuracy.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
