Alignment methodology for areas of specialization
For high-risk areas such as medical/legal, the following workflows are recommended:
- basic test:: Run generic realism benchmarks first
alignlab eval run truthfulqa --judge llm_rubric - domain enhancement:
- Add specialized quiz test sets (e.g. MedQA dataset)
- Configuring the terminology checker (added via the YAML registry)
- Mixed assessment:
- Simulating real user scenarios with alignlab-agents
- Setting Conservativeness Thresholds to Prevent Overconfident Predictions
- Comparison of domain expert labeling results calibration scoring criteria
A healthcare AI team's practice showed that the combination of TruthfulQA and professional reviews reduced the model hallucination rate from 18% to 5%. the key is to report on the confidence_interval Observe indicator stability in the data.
This answer comes from the articleAlignLab: A Comprehensive Toolset for Aligning Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " How to optimize realism metrics for domain-specific models?
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