Fine-Tuning Large Language Models for Environmental Health and Safety Applications

This study aims to simplify Environmental Health and Safety (EHS) by leveraging the power of Large Language Models (LLMs). In this research, we focus on fine-tuning three LLMs — Llama, Mistral, and Falcon — using PEFT techniques such as QLoRA and SFT, to address domain-specific needs such as safety compliance, incident reporting, and knowledge dissemination. Our research methodology involves fine-tuning each LLM model on a custom dataset compiled from various regulatory agencies, supplemented by targeted web scraping and manual collection of questionnaires to capture and enrich the models with the latest regulations and guidelines. This study aims to compare the effectiveness of these fine-tuned models to identify the most effective model and fine-tuning techniques for specific EHS applications. We found that Mistral outperformed Falcon and Llama on ROUGE, BERTscore, and a manual evaluation.