Sunday 06 April 2025
The quest for confidence in AI models has long been a thorny issue. Researchers have struggled to develop methods that can accurately gauge a model’s uncertainty, leading to overconfidence and poor performance. A new paper published today sheds light on this problem by investigating the effects of confidence elicitation before or after an answer.
For years, researchers have employed various techniques to elicit confidence from AI models, such as verbalized confidence prompts or explicit calibration methods. However, these approaches often fail to produce reliable results, leading to overestimation of a model’s accuracy. The authors of this paper aim to address this issue by exploring the impact of placing confidence elicitation before or after an answer.
The researchers conducted two experiments: one using vanilla GPT-3.5 with verbalized confidence prompts before and after answers, and another combining their method with GPT-3.5. The results show that confidence elicitation before answers tends to produce more conservative predictions, with smaller mean confidences. This suggests that confidence before answers could lead to better calibration in vanilla verbalized confidence elicitation.
However, when the authors combined their method with GPT-3.5, they found no significant difference between placing confidence elicitation before or after an answer. The average AUROC difference narrowed to 2.1% points (64.6% vs 66.7%), while ECE values remained within 1.1% points (20.0% vs 21.1%). Domain-specific patterns emerged, with confidence-after yielding better calibration in some areas and worse calibration in others.
The findings suggest that the placement of confidence elicitation has a nuanced impact on performance, but it is not a critical factor for overall results. The authors’ method, which incorporates confidence manipulation and steered answer selection, remains effective regardless of whether confidence is elicited before or after an answer.
This research has significant implications for the development of reliable AI models. By understanding how to elicit accurate confidence from these models, we can better evaluate their performance and make more informed decisions. The authors’ work highlights the importance of considering multiple factors when designing confidence elicitation methods, rather than relying on a single approach.
As researchers continue to explore new techniques for improving model confidence, this study provides valuable insights into the effects of placing confidence elicitation before or after an answer. By understanding these subtleties, we can move closer to developing AI models that accurately gauge their own uncertainty and make more reliable predictions.
Cite this article: “Steering Confidence in Large Language Models: A Study on Prompt Engineering and Calibration”, The Science Archive, 2025.
Ai, Confidence Elicitation, Model Calibration, Gpt-3.5, Uncertainty Estimation, Machine Learning, Overconfidence, Performance Evaluation, Reliability, Prediction Accuracy
