Papers by William Thorne
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes (2025.findings-emnlp)
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| Challenge: | Existing work on humour explanation has focused on short pun-based jokes, but Large Language Models (LLMs) are not capable of generating adequate explanations of all joke types. |
| Approach: | They compare the ability of Large Language Models (LLMs) to explain humour from simple puns to complex topical humor that requires esoteric knowledge of real-world entities and events. |
| Outcome: | The proposed models are incapable of generating adequate explanations of all joke types, highlighting the narrow focus of most existing work on overly simple joke forms. |
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science (2024.lrec-main)
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Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
| Challenge: | Existing instruction-tuned Large Language Models (LLMs) have impressive language understanding and the capacity to generate responses that follow specific prompts. |
| Approach: | They evaluate the zero-shot performance of two publicly accessible LLMs, ChatGPT and OpenAssistant, in the context of six Computational Social Science classification tasks. |
| Outcome: | The proposed LLMs perform better than state-of-the-art models on social science tasks. |