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arXiv · 2311.06377

Heaps' Law in GPT-Neo Large Language Model Emulated Corpora

Uyen Lai·Gurjit S. Randhawa·Paul Sheridan
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Abstract

Heaps' law is an empirical relation in text analysis that predicts vocabulary growth as a function of corpus size. While this law has been validated in diverse human-authored text corpora, its applicability to large language model generated text remains unexplored. This study addresses this gap, focusing on the emulation of corpora using the suite of GPT-Neo large language models. To conduct our investigation, we emulated corpora of PubMed abstracts using three different parameter sizes of the GPT-Neo model. Our emulation strategy involved using the initial five words of each PubMed abstract as a prompt and instructing the model to expand the content up to the original abstract's length. Our findings indicate that the generated corpora adhere to Heaps' law. Interestingly, as the GPT-Neo model size grows, its generated vocabulary increasingly adheres to Heaps' law as as observed in human-authored text. To further improve the richness and authenticity of GPT-Neo outputs, future iterations could emphasize enhancing model size or refining the model architecture to curtail vocabulary repetition.

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Uyen Lai, Gurjit S. Randhawa, Paul Sheridan. 2023-11-10. Heaps' Law in GPT-Neo Large Language Model Emulated Corpora. https://arxiv.org/abs/2311.06377

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