Fine-tuning large language models for interdisciplinary environmental challenges

Abstract

Large language models (LLMs) are revolutionizing specialized fields by enabling advanced reasoning and data synthesis. Environmental science, however, poses unique hurdles due to its interdisciplinary scope, specialized jargon, and heterogeneous data from climate dynamics to ecosystem management. Despite progress in subdomains like hydrology and climate modeling, no integrated framework exists to generate high-quality, domain-specific training data or evaluate LLM performance across the discipline. Here we introduce a unified pipeline to address this gap. It comprises EnvInstruct, a multi-agent system for prompt generation; ChatEnv, a balanced 100-million-token instruction dataset spanning five core themes (climate change, ecosystems, water resources, soil management, and renewable energy); and EnvBench, a 4998-item benchmark assessing analysis, reasoning, calculation, and description tasks. Applying this pipeline, we fine-tune an 8-billion-parameter model, EnvGPT, which achieves 92.06 ± 1.85 % accuracy on the independent EnviroExam benchmark—surpassing the parameter-matched LLaMA-3.1–8B baseline by ∼8 percentage points and rivaling the closed-source GPT-4o-mini and the 9-fold larger Qwen2.5–72B. On EnvBench, EnvGPT earns top LLM-assigned scores for relevance (4.87 ± 0.11), factuality (4.70 ± 0.15), completeness (4.38 ± 0.19), and style (4.85 ± 0.10), outperforming baselines in every category. This study reveals how targeted supervised fine-tuning on curated domain data can propel compact LLMs to state-of-the-art levels, bridging gaps in environmental applications. By openly releasing EnvGPT, ChatEnv, and EnvBench, our work establishes a reproducible foundation for accelerating LLM adoption in environmental research, policy, and practice, with potential extensions to multimodal and real-time tools.