AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
Keywords: Machine Creativity, Large Language Model, Science of LLM, Machine Text Detection
TL;DR: We present CREATIVITY INDEX, a metric that quantifies the creativity of a text by reconstructing it from existing web snippets, supported by a novel dynamic programming algorithm, DJ SEARCH, for efficient computation.
Abstract: Creativity has long been considered one of the most difficult aspect of human intelligence for AI to mimic. However, the rise of Large Language Models (LLMs), like ChatGPT, has raised questions about whether AI can match or even surpass
human creativity. We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text by reconstructing it from existing text snippets on the web. CREATIVITY INDEX is motivated by the hypothesis that the seemingly remarkable creativity of LLMs may be attributable in large part to the creativity of human-written texts on the web. To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm that can search verbatim and near-verbatim matches of text snippets from a given document against the web. Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2% higher than that of LLMs, and that alignment reduces the CREATIVITY INDEX of LLMs by an average of 30.1%. In addition, we explore variations in the CREATIVITY INDEX among different human authors and discuss the potential factors contributing to these differences. Finally, we showcase a novel application of CREATIVITY INDEX for zero-shot machine text detection, where it proves to be surprisingly effective—outperforming the strong zero-shot system DetectGPT by a substantial margin of 30.2%, and even surpassing a leading supervised system, GhostBuster, in five out of six domains.
Primary Area: interpretability and explainable AI
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Submission Number: 3478
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