2 citations · 6 across the 4 of their papers we have counts for
4 papers
Can Large Language Models Outperform Non-Experts in Poetry Evaluation? A Comparative Study Using the Consensual Assessment Technique
Piotr Sawicki, Marek Grześ, Dan Brown +1
This study adapts the Consensual Assessment Technique (CAT) for Large Language Models (LLMs), introducing a novel methodology for poetry evaluation. Using a 90-poem dataset with a…
Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording
Aisha Khatun, Daniel G. Brown
Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs ar…
Bits of Grass: Does GPT already know how to write like Whitman?
Piotr Sawicki, Marek Grzes, Fabricio Goes +3
This study examines the ability of GPT-3.5, GPT-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which…
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as Judges
Fabricio Goes, Zisen Zhou, Piotr Sawicki +2
This paper presents the Crowd Score, a novel method to assess the funniness of jokes using large language models (LLMs) as AI judges. Our method relies on inducing different person…