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Large language models reduce public knowledge sharing on online Q&A platforms.

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Large language models (LLMs) like ChatGPT are reducing human-generated content on platforms like Stack Overflow. This decline in data could hinder the development of future AI models.

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Data Science

Background:

  • Large language models (LLMs) offer potential as substitutes for human-generated data.
  • The increasing use of LLMs may reduce the availability of human-generated content crucial for training future AI models.

Purpose of the Study:

  • To investigate the impact of large language models (LLMs), specifically ChatGPT, on human-generated content production.
  • To quantify the reduction in activity on Stack Overflow following ChatGPT's release.

Main Methods:

  • Documented a decrease in Stack Overflow activity post-ChatGPT release.
  • Utilized counterfactuals comparing Stack Overflow to resources less affected by ChatGPT (e.g., Russian/Chinese counterparts, mathematics forums).
  • Analyzed changes in post quality and content creation across user experience levels.

Main Results:

  • Stack Overflow activity declined by 25% within six months of ChatGPT's release, relative to unaffected platforms.
  • The decline was more pronounced for posts related to popular programming languages.
  • No significant change in post quality was observed; content creation decreased across all user experience levels.

Conclusions:

  • LLMs like ChatGPT are displacing not only low-quality or beginner content but also content from experienced users.
  • The widespread adoption of LLMs is reducing the production of public data essential for training future AI models, posing significant challenges.
  • This reduction in training data has critical implications for the continued advancement of AI development.