Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Evaluating Confidence-Gated Retrieval with Matched Trajectory Replay
TL;DR
Matched trajectory replay is introduced as a method to evaluate confidence-gated retrieval in language models. It reveals that calibration can enhance accuracy but may reduce coverage and increase retrieval costs.
✦ Why It Matters
Implement matched trajectory replay to better evaluate confidence in your language models today.
Key Takeaways
- Use matched trajectory replay to assess confidence in language models.
- Consider the trade-off between accuracy and coverage when calibrating confidence.
- Evaluate retrieval costs alongside accuracy improvements.
- Implement separate utility assessments for retrieval decisions.
How It Works
Matched trajectory replay fixes variables like candidate answers and action costs to isolate the effects of confidence on decision-making. This allows for a clearer comparison of how different confidence evaluations impact the actions taken by language models.