What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification

Cross-source adaptation study finds simpler LLM strategies transfer more reliably for climate disclosure classification

A new arXiv preprint reframes climate disclosure classification as a cross-source adaptation problem, evaluating three LLM adaptation strategies — definitions, few-shot examples, and fine-tuning — across eleven open- and closed-source models using two corpora from different document sources (annual reports, press releases, earnings calls). The key finding is that stronger in-source strategies (similarity-based retrieval, LoRA fine-tuning) lose their advantage under source shift, while simpler approaches like random few-shot selection and well-matched definitions transfer more consistently. The result has practical implications for NLP practitioners deploying classifiers across heterogeneous document types.