5 papers
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…
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…
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…
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…