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Does Writing with Language Models Reduce Content Diversity?

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arxiv 2309.05196 v3 pith:RLY4B2GA submitted 2023-09-11 cs.CL cs.CYcs.HCcs.LG

Vishakh Padmakumar , He He This is my paper · ORCID
classification cs.CLcs.CYcs.HCcs.LG
keywords diversitycontentmodelwritingdiverseinstructgptmodelsdifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.

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Cited by 11 Pith papers

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