6 papers · 1 filter
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…
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…
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…
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…
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…