5 papers · 1 filter
PTCG: Persona-guided Tree-based Counterargument Generation
Eunbeen Son, Yohan Jo, Joonsuk Park +1
The ability to generate counterarguments is important for critical thinking and balanced discourse, yet existing approaches typically produce only a single counterargument, failing…
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
Maja Stahl, Timon Ziegenbein, Joonsuk Park +1
Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilit…
Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization
Miyoung Ko, Sue Hyun Park, Joonsuk Park +1
Despite the advances in large language models (LLMs), how they use their knowledge for reasoning is not yet well understood. In this study, we propose a method that deconstructs co…
Argument Quality Assessment in the Age of Instruction-Following Large Language Models
Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio +5
The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, wri…
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Dongjun Kang, Joonsuk Park, Yohan Jo +1
Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing. However, conducting large-…