Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow (2307.07367v1)

Abstract: LLMs like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. This substitution can present a significant problem in securing training data for future models. In this work, we investigate how the release of ChatGPT changed human-generated open data on the web by analyzing the activity on Stack Overflow, the leading online Q&A platform for computer programming. We find that relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable, activity on Stack Overflow significantly decreased. A difference-in-differences model estimates a 16\% decrease in weekly posts on Stack Overflow. This effect increases in magnitude over time, and is larger for posts related to the most widely used programming languages. Posts made after ChatGPT get similar voting scores than before, suggesting that ChatGPT is not merely displacing duplicate or low-quality content. These results suggest that more users are adopting LLMs to answer questions and they are better substitutes for Stack Overflow for languages for which they have more training data. Using models like ChatGPT may be more efficient for solving certain programming problems, but its widespread adoption and the resulting shift away from public exchange on the web will limit the open data people and models can learn from in the future.

• The paper reveals that ChatGPT’s release correlates with a 16% to 25% decline in Stack Overflow posts, as evidenced by a difference-in-differences model.
• The analysis shows that the reduction in user contributions affects posts across quality levels, with more popular programming languages like Python and JavaScript experiencing steeper drops.
• The study warns that decreased open data creation could impede future AI training and promote an oligopoly by favoring proprietary datasets over public digital goods.

Analyzing the Impact of LLMs on Open Data: Insights from Stack Overflow

The rapid development of LLMs, such as ChatGPT, has introduced transformative changes within various sectors by providing efficient information retrieval mechanisms. However, these technological advancements raise pertinent questions concerning their influence on public knowledge repositories which have traditionally provided digital public goods. The presented paper explores this issue by assessing whether ChatGPT poses a threat to data generation on public platforms, focusing primarily on Stack Overflow, a well-established question-and-answer ecosystem for software development.

Summary and Key Findings

The paper embarks on an empirical investigation to quantify the influence of ChatGPT on user-generated content by employing a difference-in-differences model. Leveraging data from Stack Overflow and comparable platforms unaffected by ChatGPT, such as Russian and Chinese language equivalents along with mathematics-centric sites, the paper reveals a substantial 16% decrease in posting activity on Stack Overflow following ChatGPT's release. This decrease, which amplified to approximately 25% over six months, highlights ChatGPT's displacement of traditional modes of interaction in programming communities.

Notably, the paper dismisses the notion that this reduction pivots primarily on the displacement of low-quality content. Analyses of social feedback—upvotes and downvotes—show no significant post-ChatGPT change, suggesting a blanket reduction across post quality levels. Furthermore, the paper identifies heterogeneous effects among programming languages with more popular ones on GitHub, like Python and JavaScript, exhibiting greater declines. This correlation underscores the proficiency of ChatGPT with languages abundant in training data, contributing to its substitutive capacity.

Implications and Speculations on Future Developments

The findings provoke discussions regarding the sustainability of the AI ecosystem, particularly concerning training data for future models. A vital implication is that the downturn in open data creation might stymie the developmental cycle of succeeding models. Despite the growing efficiency of LLMs like ChatGPT, reliance on user interactions—which remain private—may impose constraints on learning resources, limiting innovation and diversity in AI advancements.

Additionally, the dominance of models such as ChatGPT could monopolize user interaction data, granting entities like OpenAI a compounded advantage over potential entrants and maintaining an oligopoly in the AI landscape. This consolidation could stifle competition, perpetuating an opaque environment rather than the open-data paradigm historically seen with web repositories.

In wider socioeconomic contexts, these shifts might marginalize digital public goods, reallocating benefits towards proprietary datasets. Such a transformation risks exiting individuals from participatory knowledge ecosystems, ultimately narrowing the scope and accessibility of information sharing.

Conclusion and Call to Action

This paper underscores a nuanced interplay between technological advancements and the digital commons. While LLMs like ChatGPT undoubtedly optimize information retrieval processes, ensuring their sustainable coexistence with public knowledge repositories necessitates strategic intervention. This research not only highlights the need for reevaluating AI deployment strategies to preserve digital public goods but also advocates for the promotion of equitable data access frameworks to facilitate ongoing knowledge democratization.

The paper also fosters crucial dialogues about implementing incentivization mechanisms to sustain contributions to public digital resources. As research ventures further into the impacts of LLMs, fostering a multidisciplinary approach encompassing economics, policy-making, and technology will be paramount to shaping a cohesive and thriving information society.