1 citations · 1 across the 14 of their papers we have counts for
15 papers
Retrieved but Not Read: Understanding Why Search-Augmented LLMs Fail to Use Available Evidence
Owen Walters, Lihu Chen, Francesca Toni
Web search allows LLMs to access up-to-date information, but it remains unclear why they sometimes produce outdated answers even when relevant evidence is available. To study this…
Embedding Models for Stance-Aware Argument Retrieval
Angelo Sparacino, Francesca Toni, Adam Dejl
In computational argumentation, obtaining arguments that explicitly support or attack given claims is a critical precursor to downstream reasoning tasks. When these supporting and…
A Theory of Post-hoc Debate Judgement
Xiang Yin, Adam Dejl, Antonio Rago +2
Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engagement. For example, LLM-empowered agents…
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…
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…
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…
ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models
Adam Dejl, Deniz Gorur, Francesca Toni
Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation for decision-making, with the aim of making the result…
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…
Latent Debate: A Surrogate Framework for Interpreting LLM Thinking
Lihu Chen, Xiang Yin, Francesca Toni
Understanding the internal thinking process of Large Language Models (LLMs) and the cause of hallucinations remains a key challenge. To this end, we introduce latent debate, a nove…
Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models
Kevin Zhou, Adam Dejl, Gabriel Freedman +3
Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We ex…
MArgE: Meshing Argumentative Evidence from Multiple Large Language Models for Justifiable Claim Verification
Ming Pok Ng, Junqi Jiang, Gabriel Freedman +2
Leveraging outputs from multiple large language models (LLMs) is emerging as a method for harnessing their power across a wide range of tasks while mitigating their capacity for ma…
Exploring the Potential for Large Language Models to Demonstrate Rational Probabilistic Beliefs
Gabriel Freedman, Francesca Toni
Advances in the general capabilities of large language models (LLMs) have led to their use for information retrieval, and as components in automated decision systems. A faithful re…
Pub-Guard-LLM: Detecting Retracted Biomedical Articles with Reliable Explanations
Lihu Chen, Shuojie Fu, Gabriel Freedman +6
A significant and growing number of published scientific articles is found to involve fraudulent practices, posing a serious threat to the credibility and safety of research in fie…