Did ChatGPT displace only low-quality Stack Overflow posts?
After ChatGPT's release, Stack Overflow posts received similar vote scores, but votes are an imperfect quality measure. The research uses voting patterns to infer what type of content was displaced, though this inference remains unvalidated against expert judgment.
The second finding concerns what was displaced. Votes are the excerpt's only quality signal, described as "simple forms of social feedback provided by other users to rate posts." The introduction reports "no change in the votes posts receive on Stack Overflow since the release of ChatGPT," and concludes that ChatGPT "is displacing a wide variety of Stack Overflow posts, including high-quality content." The discussion uses softer wording, "no large change in social feedback," and reads it as "suggesting that average post quality has not changed."
The inference rests on votes standing in for quality. The abstract states the result plainly: posts made after ChatGPT "get similar voting scores than before," which the paper says suggests ChatGPT "is not merely displacing duplicate or low-quality content." To argue that duplicates cannot explain the drop, the paper puts duplicates at "only 3% of posts," citing Correa and Sureka (2013), and notes that it does not observe significant changes in voting outcomes.
Against the nearest notes, this sits next to Can crowdsourced votes reliably rank language models?. There, crowd votes earn credibility because they agree with expert raters. This excerpt has no such validation for Stack Overflow votes, and it says so: quality can be assessed "only partially" through up- and downvotes. The first note in this pair covers how much posting fell. This one asks what was lost, and its answer depends on a proxy the excerpt does not check against expert judgment.
The excerpt raises the obvious alternative: "Users may be posting more challenging questions, ones that LLMs cannot (yet) address." It leaves this to future work, asking whether later activity "is more complex or sophisticated on average," and does not test it. So the flat votes support a narrower claim than "only weak content left." By this measure the decline is not confined to weak posts, but the excerpt cannot say whether the remaining or lost questions were harder. That matters for the commons, because the open pool's value to future models depends on what it still contains, and the excerpt leaves that unanswered.
Inquiring lines that read this note 10
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
How can AI systems reliably guide voters without introducing political bias?- Can users reach ChatGPT in regions where it is officially unavailable?
- How much training data did ChatGPT receive from Stack Overflow?
- What other events affected Stack Overflow during the measurement window?
- How could Stack Overflow votes be validated against expert quality assessments?
- Are unanswered questions on Stack Overflow becoming more difficult after ChatGPT?
- What proportion of Stack Overflow's lost posts were genuinely high-quality?
- Does ChatGPT displace search engines or question-and-answer platforms?
- Why did Upwork freelancers lose earnings after ChatGPT's release?
Related concepts in this collection 2
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Can crowdsourced votes reliably rank language models?
Explores whether large-scale human preference voting from casual users produces valid model rankings comparable to expert judgment, and what makes such crowdsourced evaluation trustworthy at scale.
crowd votes are validated against expert raters there; Stack Overflow votes go unvalidated here.
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Did ChatGPT cause Stack Overflow posting to decline?
Researchers used a difference-in-differences model to test whether public programming Q&A posting fell after ChatGPT's release. This matters because it could signal whether AI tools are shifting knowledge from public commons to private use.
the decline itself; this note covers the composition of what was lost.
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow
- Prioritize Economy or Climate Action? Investigating ChatGPT Response Differences Based on Inferred Political Orientation
- Strengthening ChatGPT's responses in sensitive conversations
- The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis
- Mapping the Increasing Use of LLMs in Scientific Papers
- Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit
- Do We Trust ChatGPT as much as Google Search and Wikipedia?
- AI and Elections: How Well Do AI Platforms Answer Voter Questions?
Original note title
Stack Overflow vote scores showed no large change after ChatGPT's release — the paper suggests displacement reaches beyond low-quality posts