Estimating Uncertainty in Classifier Performance with Applications to Large Language Models and Nested Data
arXiv:2606.26422v1 Announce Type: new Abstract: Researchers increasingly use text classification--supervised models or large language models--to measure constructs from natural language, providing metrics such as recall and precision as evidence of their validity. Yet, though these metrics are point estimates subject to sampling variation, measures of uncertainty are inconsistently reported alongside them. Further, when they are reported, they are often estimated with methods that are not appropriate when relevant labelled datasets are small or performance is high. To increase and improve conf
The proliferation of Large Language Models and text classification tools necessitates robust methods for evaluating their performance and reliability, especially as they integrate into critical applications.
Accurate and reliable uncertainty estimation in AI classifier performance is crucial for developing trustworthy AI systems, making informed decisions based on AI outputs, and ensuring the validity of AI-driven research.
The focus on more appropriate uncertainty estimation for AI classifiers, particularly with small datasets or high performance, will lead to more nuanced and credible assessments of AI model capabilities.
- · AI researchers
- · AI developers
- · Organizations relying on text classification
- · Developers of unreliable AI models
- · Researchers using inadequate evaluation metrics
Improved reliability and trustworthiness of AI models, particularly Large Language Models, due to better performance evaluation.
Reduced incidence of AI failures or misinterpretations in critical applications, fostering greater adoption and reliance on AI.
Potential for new regulatory standards and best practices for AI model validation that incorporate robust uncertainty quantification.
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Read at arXiv cs.AI