What is it about?
When people search workplace chats and messages, search systems often learn from noisy click data. We combined AI's understanding of meaning with real user behavior, keeping only examples where both agreed. This helps people find more relevant information.
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Why is it important?
Modern workplace tools like Microsoft Teams and Copilot help people find information from millions of messages, but training these systems is difficult because human review is limited by privacy and clicks alone can be misleading. Our approach improves the quality of the training data by using only examples where both AI and user behavior agree. This leads to more accurate search results and better citations in AI assistants, helping people find trustworthy information faster without changing the underlying search system.
Perspectives
I found this work interesting because it addresses a practical challenge faced by AI-powered workplace search systems. Rather than building a more complex model, the authors improve the quality of the data used for training by combining human behavior with AI-generated judgments. The idea is simple, scalable, and supported by real-world deployment, making it relevant for organizations building reliable AI assistants.
Rohan Mallick
Microsoft Corp
Read the Original
This page is a summary of: LLM-Click Agreement: Harmonizing Implicit Feedback and Semantic Judgments for Enterprise Search, July 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3805712.3808394.
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