Causal language is a window into how humans reason causally and counterfactually, a capacity widely held to be the hallmark of human intelligence, and a key topic in research on science and crisis communication, mis/dis-information, and public trust and solidarity. The project leverages state-of-the-art large language models (LLMs) and few-shot prompt-based training to implement a ground-breaking computationally-assisted approach to Causal Relation Extraction (CRE) at scale: modeling collectively constructed causal models via causal linguistic reports in texts. It represents the first NLP implementation of causal modeling at scale and is developed with multilingual support for both English and Danish. By developing methods to automate the extraction of collective causal models from corpora and produce interpretable graphs of their underlying structure, we allow causal relations to be investigated empirically and at the scale of public discourse.

LLMs in Language and Cognition Research