Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
📰 ArXiv cs.AI
Learn to predict query-level rejection risk in clinical LLM systems to improve real-world utility and user acceptance
Action Steps
- Build a dataset of query-level annotations to train a rejection risk predictor
- Configure a clinical LLM system to integrate with the predictor
- Test the predictor on a held-out set of queries to evaluate its performance
- Apply the predictor to identify high-risk queries and reject them
- Compare the performance of the clinical LLM system with and without the predictor
Who Needs to Know This
Data scientists and clinicians on a team can benefit from this approach to evaluate and improve the performance of clinical LLM systems, ensuring better user acceptance and real-world utility
Key Insight
💡 Predicting query-level rejection risk can significantly improve the real-world utility and user acceptance of clinical LLM systems
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🚀 Improve clinical LLM systems with deployment-centered evaluation! Predict query-level rejection risk to boost user acceptance 📈
Key Takeaways
Learn to predict query-level rejection risk in clinical LLM systems to improve real-world utility and user acceptance
Full Article
Title: Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
Abstract:
arXiv:2606.12702v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM syste
Abstract:
arXiv:2606.12702v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM syste
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