~/talk

Beyond Scoring: Rethinking Ranking in the LLM Era

Talkat TestMu Conference 2026 · 2026-08-19..08-21· [large-language-models]
name:Rhea Goel
links:none on file
slides:none on file

Modern ranking swaps handcrafted objective weights for natural language objectives and shows how to combine reasoning with behavioral prediction: start with prompting a base model, teach platform-specific signals via supervised fine-tuning, refine with pairwise preference data using direct preference optimization (or RLHF), then compress with knowledge distillation and serve cheaply via prefix caching and cascading/routing. The talk emphasizes concrete trade-offs — latency, validation, multi-objective constraints (hard vs soft) — and evaluates reasoning with LLM judges plus traditional NDCG-style metrics.

AI-written summary · about

  1. 0:00Setting the Stage: What Changes When LLMs Enter Ranking Systems
  2. 1:07Speaker Intro - Rhea Goel, Applied Science Manager, Amazon
  3. 2:08Three People, One Hotel Query, Three Correct Rankings
  4. 3:10Ranking Has Never Been About Finding One Best Item
  5. 3:41Today's Architecture: Retrieval, First-Stage Ranker, Re-Ranker
  6. 4:12When Product Requirements Become Ambiguous English Sentences
  7. 4:42What If We Optimized for Language Instead of Objective Functions?
  8. 5:43Five Questions Before Replacing a Re-Ranker With an LLM
  9. 6:13Starting Simple: Prompting a Base Model
  10. 7:15Impressive Output, but Would You Deploy It Tomorrow?
  11. 7:45Behavioural Models Predict Actions; LLMs Reason About Intent
  12. 8:46Supervised Fine-Tuning: Teaching the Model Your Platform
  13. 9:47When Imitating History Is the Wrong Objective
  14. 10:48Preference Data: Asking Which Ranking Is Better
  15. 11:50RLHF, Reward Models and the Learned Objective Function
  16. 12:22Direct Preference Optimization as the Simpler Path
  17. 13:57Multi-Objective Reality: Affordability, New Inventory, Revenue
  18. 14:29Hard Constraints vs Soft Constraints and Consistent Reasoning
  19. 16:32Making It Fast Enough: Knowledge Distillation to a Student Model
  20. 18:07Prefix Caching, Routing and Cascading
  21. 20:10Evaluating Reasoning: Beyond NDCG and MRR
  22. 20:40LLM as a Judge With Rubric, Chain of Thought and Structured Output
  23. 22:48Four Engineering Tensions That Frame the Whole Design
  24. 24:20The Real Question Isn't Replacement - It's Combination
  25. 25:23Q&A: Agentic Re-Rankers, Two Correct Answers, Continuous Improvement
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