Confidence in Classification Using LLMs and Conformal Sets (crimede-coder.com)

A recent exploration highlights the importance of measuring classification confidence in Large Language Models (LLMs), particularly for tasks like detecting explicit language in chat messages. Traditional machine learning models typically require training on specific tasks, whereas LLMs can generalize across various text categories without explicit training. However, a significant challenge arises in determining the confidence of these classifications. The article emphasizes using log probabilities returned by LLMs, which are often not calibrated for arbitrary tasks, to manage false positives and recall levels effectively. This discussion integrates the concept of conformal inference, allowing practitioners to set appropriate thresholds for classification based on desired recall rates or acceptable false positive levels. By leveraging a case study involving the Jigsaw toxic comment classification competition, the author demonstrates how to process sample comments, analyze classification outcomes, and adjust thresholds. The research suggests that, despite the inherent overconfidence in LLM probabilities, applying conformal methods can provide useful estimates for managing false positives in production settings, thus enhancing the reliability of LLM-powered applications in real-world scenarios.