12 papers
Superficial Beliefs in LLM Decision-Making
Gabriel Freedman, Francesca Toni
We ask whether large language models (LLMs) merely imitate rationales when choosing between two options, or whether their choices reflect a systematic underlying decision structure…
Neurosymbolic Learning for Inference-Time Argumentation
Gabriel Freedman, Adam Dejl, Adam Gould +4
Claim verification is an important problem in high-stakes settings, including health and finance. When information underpinning claims is incomplete or conflicting, uncertain answe…
Deep Arguing
Adam Gould, Francesca Toni
Deep learning has become the dominant approach for creating high capacity, scalable models across diverse data modalities. However, because these models rely on a large number of l…
Argumentation for Explainable and Globally Contestable Decision Support with LLMs
Adam Dejl, Matthew Williams, Francesca Toni
Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has tak…
Constrained Assumption-Based Argumentation Frameworks
Emanuele De Angelis, Fabio Fioravanti, Maria Chiara Meo +3
Assumption-based Argumentation (ABA) is a well-established form of structured argumentation. ABA frameworks with an underlying atomic language are widely studied, but their applica…
Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us
Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni +1
Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and ext…