2 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priors
Qiming Bao, Xiaoxuan Fu, Michael Witbrock
Large language models (LLMs) achieve high accuracy on many reasoning benchmarks but remain brittle under structural perturbations of rule-based systems. We introduce a diagnostic f…
Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithm
Yang Chen, Menglin Zou, Jiaqi Zhang +6
Inverse Reinforcement Learning (IRL) learns a reward function to explain expert demonstrations. Modern IRL methods often use the adversarial (minimax) formulation that alternates b…
Evaluating The Impact of Stimulus Quality in Investigations of LLM Language Performance
Timothy Pistotti, Jason Brown, Michael Witbrock
Recent studies employing Large Language Models (LLMs) to test the Argument from the Poverty of the Stimulus (APS) have yielded contrasting results across syntactic phenomena. This…
Exploring Gaps in the APS: Direct Minimal Pair Analysis in LLM Syntactic Assessments
Timothy Pistotti, Jason Brown, Michael Witbrock
Recent studies probing the Argument from the Poverty of the Stimulus (APS) have applied Large Language Models (LLMs) to test the learnability of complex syntax through surprisal-ba…
Psychology-Driven Enhancement of Humour Translation
Yuchen Su, Yonghua Zhu, Yang Chen +2
Humour translation plays a vital role as a bridge between different cultures, fostering understanding and communication. Although most existing Large Language Models (LLMs) are cap…
A Survey of Pun Generation: Datasets, Evaluations and Methodologies
Yuchen Su, Yonghua Zhu, Ruofan Wang +3
Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness,…
Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds
Gaël Gendron, Jože M. Rožanec, Michael Witbrock +1
Causal world models are systems that can answer counterfactual questions about an environment of interest, i.e. predict how it would have evolved if an arbitrary subset of events h…