4 papers
Explanation Generation for Contradiction Reconciliation with LLMs
Jason Chan, Zhixue Zhao, Robert Gaizauskas
Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of human reasoning in social interacti…
Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMs
Jason Chan, Robert Gaizauskas, Zhixue Zhao
As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which to mitigate bias, errors and hal…
Position: On the Methodological Pitfalls of Evaluating Base LLMs for Reasoning
Jason Chan, Zhixue Zhao, Robert Gaizauskas
Existing work investigates the reasoning capabilities of large language models (LLMs) to uncover their limitations, human-like biases and underlying processes. Such studies include…
RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning
Jason Chan, Robert Gaizauskas, Zhixue Zhao
Formal logic enables computers to reason in natural language by representing sentences in symbolic forms and applying rules to derive conclusions. However, in what our study charac…