7 papers
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Yang Wang, Chenghao Xiao, Chia-Yi Hsiao +4
We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth" - utterances that are syntactically coherent yet pragmatically paradoxical, emotiona…
Who's Laughing Now? An Overview of Computational Humour Generation and Explanation
Tyler Loakman, William Thorne, Chenghua Lin
The creation and perception of humour is a fundamental human trait, positioning its computational understanding as one of the most challenging tasks in natural language processing…
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
Tyler Loakman, William Thorne, Chenghua Lin
Humour, as a complex language form, is derived from myriad aspects of life. Whilst existing work on computational humour has focussed almost exclusively on short pun-based jokes, w…
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
Shun Wang, Tyler Loakman, Youbo Lei +5
Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language
Shun Wang, Ge Zhang, Han Wu +3
Machine Translation (MT) has developed rapidly since the release of Large Language Models and current MT evaluation is performed through comparison with reference human translation…
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models
Tyler Loakman, Yucheng Li, Chenghua Lin
Recently, Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated aptitude as potential substitutes for human participants in experiments testing psycholin…