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AI Research
arXiv CS.AI·Jun 12·5 min read
Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
TL;DR
Large language models (LLMs) in clinical settings often face challenges in user acceptance due to inadequate evaluation methods. A pre-response classifier was developed to predict the likelihood of user rejection based on query content and deployment context.
✦ Why It Matters
Engineers can leverage deployment-specific context to enhance user acceptance of AI systems in clinical environments.
Key Takeaways
- Utilize deployment-specific context for better prediction accuracy.
- Train classifiers on user feedback to assess rejection risk.
- Focus on user acceptance rather than just correctness.
- Explore guardrails to enhance LLM response usability.
How It Works
The model predicts user rejection by analyzing both the content of the query and contextual factors like provider type and department. This dual approach allows for a more nuanced understanding of user interactions with the LLM, leading to improved prediction capabilities.