6 papers · 1 filter
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…
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,…
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…
Counterfactual Causal Inference in Natural Language with Large Language Models
Gaël Gendron, Jože M. Rožanec, Michael Witbrock +1
Causal structure discovery methods are commonly applied to structured data where the causal variables are known and where statistical testing can be used to assess the causal relat…
Can Large Language Models Learn Independent Causal Mechanisms?
Gaël Gendron, Bao Trung Nguyen, Alex Yuxuan Peng +2
Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distributio…