πApple Machine Learningβ’Stalecollected in 16h
Base LLMs Show Semantic Calibration
π‘Base LLMs semantically calibratedβno extra training needed for QA confidence!
β‘ 30-Second TL;DR
What Changed
Base LLMs calibrated on next-token but semantic unclear previously
Why It Matters
This finding enables more reliable deployment of base LLMs in QA applications without extra calibration training, potentially lowering costs. It challenges assumptions about needing fine-tuning for confidence, impacting LLM evaluation practices.
What To Do Next
Test sampling-based semantic calibration on your base LLM for QA confidence scoring.
Who should care:Researchers & Academics
Key Points
- β’Base LLMs calibrated on next-token but semantic unclear previously
- β’Sampling-based semantic calibration shows strong performance in open-domain QA
- β’Theoretical contribution establishes emergence mechanism
- β’No explicit training needed for meaningful confidence estimates
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Original source: Apple Machine Learning β