Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
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
- Incorporate deployment context for better prediction accuracy.
- Utilize pre-response classifiers in clinical AI systems.
- Focus on user acceptance, not just correctness.
- Explore guardrail applications based on rejection predictions.
Full Summary
As LLMs become more integrated into clinical systems, traditional evaluation methods often focus on correctness rather than user acceptance, leading to gaps in understanding real-world utility. To address this, a pre-response classifier was created to estimate the risk of user rejection for LLM responses, utilizing both the content of the query and specific deployment context, such as provider type and department.
Over a 4.5-month period, the model was tested against user feedback, achieving an AUROC of 0.719, indicating a good level of predictive accuracy. The study highlights the importance of incorporating deployment-specific context to enhance prediction capabilities.
Additionally, the findings suggest potential applications in implementing guardrails and improving user interactions with LLMs. This approach opens avenues for more tailored and effective clinical AI systems.