content.raw: 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 
content.type: text
content.value: 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 
content.raw: @TencentGlobal
content.type: mention
content.user: TencentGlobal
content.type: new_line
content.type: new_line
content.raw: Game-Changing Innovation: DisRanker
content.type: text
content.value: Game-Changing Innovation: DisRanker
content.type: new_line
content.raw: 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!
content.type: text
content.value: 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!
content.type: new_line
content.type: new_line
content.raw: Key Technical Highlights:
content.type: text
content.value: Key Technical Highlights:
content.type: new_line
content.raw: • Implements domain-specific Continued Pre-Training using clickstream data, treating queries as inputs to generate clicked titles and summaries
content.type: text
content.value: • Implements domain-specific Continued Pre-Training using clickstream data, treating queries as inputs to generate clicked titles and summaries
content.type: new_line
content.raw: • Uses an end-of-sequence token to represent query-document pairs during supervised fine-tuning
content.type: text
content.value: • Uses an end-of-sequence token to represent query-document pairs during supervised fine-tuning
content.type: new_line
content.raw: • Employs hybrid Point-MSE and Margin-MSE loss for knowledge distillation, optimizing both absolute scores and relative rankings
content.type: text
content.value: • Employs hybrid Point-MSE and Margin-MSE loss for knowledge distillation, optimizing both absolute scores and relative rankings
content.type: new_line
content.type: new_line
content.raw: Under the Hood:
content.type: text
content.value: Under the Hood:
content.type: new_line
content.raw: - The system first pre-trains on massive clickstream data (59M+ query-document pairs)
content.type: text
content.value: - The system first pre-trains on massive clickstream data (59M+ query-document pairs)
content.type: new_line
content.raw: - Transfers ranking expertise from a 7B parameter LLM to a compact BERT model
content.type: text
content.value: - Transfers ranking expertise from a 7B parameter LLM to a compact BERT model
content.type: new_line
content.raw: - Reduces inference latency from ~100ms to just 10ms while maintaining performance
content.type: text
content.value: - Reduces inference latency from ~100ms to just 10ms while maintaining performance
content.type: new_line
content.raw: - Achieves significant improvements:
content.type: text
content.value: - Achieves significant improvements:
content.type: new_line
content.raw: • +0.47% PageCTR
content.type: text
content.value: • +0.47% PageCTR
content.type: new_line
content.raw: • +0.58% UserCTR
content.type: text
content.value: • +0.58% UserCTR
content.type: new_line
content.raw: • +1.2% Dwell Time
content.type: text
content.value: • +1.2% Dwell Time
content.type: new_line
content.type: new_line
content.raw: Real-World Impact:
content.type: text
content.value: Real-World Impact:
content.type: new_line
content.raw: Successfully integrated into production search systems as of February 2024, demonstrating that academic research can translate into practical industry solutions
content.type: text
content.value: Successfully integrated into production search systems as of February 2024, demonstrating that academic research can translate into practical industry solutions
content.type: new_line
content.type: new_line
content.raw: What are your thoughts on this breakthrough?
content.type: text
content.value: What are your thoughts on this breakthrough?
rawContent: 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?
reactions.reaction: 👀
reactions.users: John6666