Abstract: ADRs refer to harmful and unintended responses that occur when drugs are administered at normal doses for prevention,diagnosis,treatment of diseases,or regulation of physiological functions.These reactions are unexpected and can significantly impact patients’ health and physical condition.To identify ADR-related relational triples from social media and biomedical literature data—providing early warnings for patients and healthcare systems,as well as references for pharmaceutical research and drug development－this paper proposes two frameworks based on generative large language models for both low-resource and high-resource annotated data scenarios:the ADR-GPT framework,which extracts ADR relations using Chain-of-Thought promp-ting,and the CADRE-LLM framework,which leverages Chain-of-Thought-enhanced fine-tuning.It evaluates the performance of these two frameworks on the Twitter and PubMed datasets.CADRE-LLM achieves F1 score improvements of 1.51 percentage points and 1.74 percentage points over previous state-of-the-art models on the respective datasets.The low-resource ADR-GPT framework also demonstrates strong performance,outperforming fully supervised fine-tuned Qwen2.5 on the PubMed dataset.Ablation studies further validate the effectiveness of each module within the two proposed frameworks.

Key words: Relation extraction, Adverse drug reactions, Large language models, Chain of Thought(COT), Low resource