11 citations · 36 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
Mahdi Dhaini, Adam Dejl, Juraj Vladika +3
Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it re…
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…
EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation
Adam Dejl, Jonathan Pearson
Robust and comprehensive evaluation of large language models (LLMs) is essential for identifying effective LLM system configurations and mitigating risks associated with deploying…
Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation
Adam Dejl, James Barry, Alessandra Pascale +1
Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce outputs that are incomplete or se…
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…
Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts
Lihu Chen, Adam Dejl, Francesca Toni
Large Language Models (LLMs) possess vast amounts of knowledge within their parameters, prompting research into methods for locating and editing this knowledge. Previous work has l…
Argumentative Large Language Models for Explainable and Contestable Claim Verification
Gabriel Freedman, Adam Dejl, Deniz Gorur +3
The profusion of knowledge encoded in large language models (LLMs) and their ability to apply this knowledge zero-shot in a range of settings makes them promising candidates for us…
RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction
Sameer Khanna, Adam Dejl, Kibo Yoon +4
We present RadGraph2, a novel dataset for extracting information from radiology reports that focuses on capturing changes in disease state and device placement over time. We introd…