大型语言模型是否对数字公共产品构成威胁？来自 Stack Overflow 活动的证据
Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow
AI总结：
本文以 Stack Overflow 活动数据为实证依据，利用双重差分法量化 ChatGPT 发布对在线编程问答平台的影响，发现其导致每周发帖量显著下降16%，揭示了大型语言模型对数字公共产品和未来训练数据构成的潜在威胁。
摘要：
像 ChatGPT 这样的大型语言模型能够高效地为用户提供各种主题的信息，这使其成为网络搜索和在线向他人求助的潜在替代品。但由于用户与模型的交互是私密的，这些模型可能会大幅减少公开可用的人类生成数据和知识资源。这种替代可能对未来模型的训练数据获取构成重大问题。在本研究中，我们通过分析 Stack Overflow（领先的计算机编程在线问答平台）上的活动，考察了 ChatGPT 的发布如何改变了网络上人类生成的开放数据。我们发现，相对于访问 ChatGPT 受限的俄语和中文同类平台，以及 ChatGPT 能力较弱的数学类类似论坛，Stack Overflow 上的活动显著下降。双重差分模型估计 Stack Overflow 的每周发帖量下降了 16%。这一效应随时间推移而增强，并且对于与最广泛使用的编程语言相关的帖子更为明显。ChatGPT 发布后的帖子获得的投票分数与之前相似，这表明 ChatGPT 并非仅仅取代了重复或低质量的内容。这些结果表明，越来越多的用户正在采用大型语言模型来回答问题，并且对于模型拥有更多训练数据的编程语言而言，这些模型是 Stack Overflow 的更好替代品。使用像 ChatGPT 这样的模型在解决某些编程问题时可能更高效，但其广泛采用以及由此导致的网络公开交流的转移，将限制未来人们和模型可以从中学习的开放数据。
英文摘要：
Large language models 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 large language models 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
