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Found 6 papers, 1 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.
DeepG2P: Fusing Multi-Modal Data to Improve Crop Production
Agriculture is at the heart of the solution to achieve sustainability in feeding the world population, but advancing our understanding on how agricultural output responds to climatic variability is still needed.
Causal Modeling of Soil Processes for Improved Generalization
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.
Machine learning can guide experimental approaches for protein digestibility estimations
Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins.
Machine learning approaches for localized lockdown during COVID-19: a case study analysis
Countries like Brazil have had difficulty in dealing with the virus due to the high socioeconomic difference of states and municipalities.
