10 citations · 17 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…