9 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…
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond
Rubing Chen, Jiaxin Wu, Jian Wang +5
The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle…
Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
Chi Wang, Min Gao, Zongwei Wang +3
With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent ne…
Advancing Dialectal Arabic to Modern Standard Arabic Machine Translation
Abdullah Alabdullah, Lifeng Han, Chenghua Lin
Dialectal Arabic (DA) poses a persistent challenge for natural language processing (NLP), as most everyday communication in the Arab world occurs in dialects that diverge significa…
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…
Parallel Branch Model Predictive Control on GPUs
Luyao Zhang, Chenghuai Lin, Sergio Grammatico
We present a GPU-based solver for trajectory planning problems using branch Model Predictive Control. Building on iterative LQR methods, we adopt a multiple-shooting formulation fo…
Natural Language Generation
Emiel van Miltenburg, Chenghua Lin
This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of s…
Increasing the Difficulty of Automatically Generated Questions via Reinforcement Learning with Synthetic Preference
William Thorne, Ambrose Robinson, Bohua Peng +2
As the cultural heritage sector increasingly adopts technologies like Retrieval-Augmented Generation (RAG) to provide more personalised search experiences and enable conversations…