Causal information extraction with LLMs
Organization
Tartu NLP
Abstract
With the recent increase in performance and widespread use of LLMs, many avenues are possible for their implementation as support tools. Since processing large texts and reading between the lines (literally) can be time-consuming for humans, couldn't this process be assisted? The goal of this thesis is to investigate the efficacy of LLM architectures in extracting "Why?/How?"-questions based on information from textual data, with different possible procedures.
Graduation Theses defence year
2024-2025
Supervisor
Giacomo Magnifico
Spoken language (s)
English
Requirements for candidates
Level
Bachelor, Masters
Application of contact
Name
Giacomo Magnifico
Phone
E-mail