Utilizing LLMs for Enhanced Argumentation and Extraction of Causal Knowledge from Scientific Literature

Current semantic extraction tools have limited performance in identifying causal relations, neglecting variations in argument quality, especially persuasive strength across different sentences. The present study proposes a five-element based (evidence cogency, concept, relation stance, claim-context relevance, conditional information) causal knowledge mining framework and automatically implements it using large language models (LLMs) to improve the understanding of disease causal mechanisms. As a result, regarding cogency evaluation, the accuracy (0.84) of the fine-tuned Llama2-7b largely exceeds the accuracy of GPT-3.5 turbo with few-shot. Regarding causal extraction, by combining PubTator and ChatGLM, the entity first-relation later extraction (recall, 0.85) outperforms the relation first-entity later means (recall, 0.76), performing great in three outer validation sets (a gestational diabetes-relevant dataset and two general biomedical datasets), aligning entities for further causal graph construction. LLMs-enabled scientific causality mining is promising in delineating the causal argument structure and understanding the underlying mechanisms of a given exposure-outcome pair.