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Exciting breakthrough in Search Engine Technology! Just read a fascinating paper on "Best Practices for Distilling Large Language Models into BERT for Web Search Ranking" from
@TencentGlobal
Game-Changing Innovation: DisRanker
A novel distillation pipeline that combines the power of Large Language Models with BERT's efficiency for web search ranking - now deployed in commercial search engines!
Key Technical Highlights:
• Implements domain-specific Continued Pre-Training using clickstream data, treating queries as inputs to generate clicked titles and summaries
• Uses an end-of-sequence token to represent query-document pairs during supervised fine-tuning
• Employs hybrid Point-MSE and Margin-MSE loss for knowledge distillation, optimizing both absolute scores and relative rankings
Under the Hood:
- The system first pre-trains on massive clickstream data (59M+ query-document pairs)
- Transfers ranking expertise from a 7B parameter LLM to a compact BERT model
- Reduces inference latency from ~100ms to just 10ms while maintaining performance
- Achieves significant improvements:
• +0.47% PageCTR
• +0.58% UserCTR
• +1.2% Dwell Time
Real-World Impact:
Successfully integrated into production search systems as of February 2024, demonstrating that academic research can translate into practical industry solutions
What are your thoughts on this breakthrough?
Game-Changing Innovation: DisRanker
A novel distillation pipeline that combines the power of Large Language Models with BERT's efficiency for web search ranking - now deployed in commercial search engines!
Key Technical Highlights:
• Implements domain-specific Continued Pre-Training using clickstream data, treating queries as inputs to generate clicked titles and summaries
• Uses an end-of-sequence token to represent query-document pairs during supervised fine-tuning
• Employs hybrid Point-MSE and Margin-MSE loss for knowledge distillation, optimizing both absolute scores and relative rankings
Under the Hood:
- The system first pre-trains on massive clickstream data (59M+ query-document pairs)
- Transfers ranking expertise from a 7B parameter LLM to a compact BERT model
- Reduces inference latency from ~100ms to just 10ms while maintaining performance
- Achieves significant improvements:
• +0.47% PageCTR
• +0.58% UserCTR
• +1.2% Dwell Time
Real-World Impact:
Successfully integrated into production search systems as of February 2024, demonstrating that academic research can translate into practical industry solutions
What are your thoughts on this breakthrough?