Large language models reduce public knowledge sharing on online Q&A platforms
R Maria del Rio-Chanona, Nadzeya Laurentsyeva, Johannes Wachs, Large language models reduce public knowledge sharing on online Q&A platforms, PNAS Nexus, Volume 3, Issue 9, September 2024, pgae400, https://doi.org/10.1093/pnasnexus/pgae400
Large language models (LLMs) are a potential substitute for human-generated data and knowledge resources. This substitution, however, can present a significant problem for the training data needed to develop future models if it leads to a reduction of human-generated content. In this work, we document a reduction in activity on Stack Overflow coinciding with the release of ChatGPT, a popular LLM. To test whether this reduction in activity is specific to the introduction of this LLM, we use counterfactuals involving similar human-generated knowledge resources that should not be affected by the introduction of ChatGPT to such extent. Within 6 months of ChatGPT’s release, activity on Stack Overflow decreased by 25% relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable. We interpret this estimate as a lower bound of the true impact of ChatGPT on Stack Overflow. The decline is larger for posts related to the most widely used programming languages. We find no significant change in post quality, measured by peer feedback, and observe similar decreases in content creation by more and less experienced users alike. Thus, LLMs are not only displacing duplicate, low-quality, or beginner-level content. Our findings suggest that the rapid adoption of LLMs reduces the production of public data needed to train them, with significant consequences.
This study examines the impact of ChatGPT, a large language model, on online communities that contribute to public knowledge shared on the Internet. We found that ChatGPT has led to a 25% drop in activity on Stack Overflow, a key reference website where programmers share knowledge and solve problems. This substitution threatens the future of the open web, as interactions with AI models are not added to the shared pool of online knowledge. Moreover, this phenomenon could weaken the quality of training data for future models, as machine-generated content likely cannot fully replace human creativity and insight. This shift could have significant consequences for both the public Internet and the future of AI.
