Year: 2025
Type: conference-paper
Abstract: This tutorial explores the application of Large Language Models (LLMs), such as BERT, LLAMA, andGPT-3.5/4, to the extraction of causality from text documents, including identifying causes, effects, and actions in diverse texts, such as business, medical, and newswire domains. We also address challenges relatedto data availability and quality, such as varying definitions of causality. Causality extraction plays a crucial role in natural language understanding, particularly for building structured representations of medical and technical texts and for multimodal question answering. Participants will gain access to example code and links to related repositories. Beyond causality extraction, thesession will connect these tasks to broader themes, such as the mathematics of hallucinations in generative models and best practices for effective prompting. Designed for participants with some familiarity with machine learning or natural language processing (NLP), and ideally LLMs, the tutorial should be both accessible and highly relevant.
Author: Wlodek Zadrozny
Institution: University of North Carolina at Charlotte
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410397753