Just like systems like Stack Overflow benefited from having the accepted answer bubble to the top, so too will it become important for LLMs to pull in such information for their future training data.

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LLMs need explicit user feedback mechanisms similar to Stack Overflow's "mark as answer" feature to identify which responses successfully solve problems. Without quality signals indicating whether conversations led to solutions, AI models training on user interactions cannot distinguish helpful responses from unhelpful ones. This feedback loop is critical for continued model improvement as traditional training data sources like Stack Overflow decline in activity.

Llms Need Mark as Answer