Large Language Models are Miscalibrated In-Context Learners
This addresses calibration issues in low-resource in-context learning for users of large language models, providing practical guidelines and a solution, though it is incremental as it builds on existing self-ensembling techniques.
The paper investigates whether instruction-tuned large language models achieve well-calibrated results in low-resource in-context learning setups, finding that miscalibration exists across all learning methods. It shows that self-ensembling with max probability produces robust and calibrated predictions, enhancing both task performance and calibration.
When adapting ICL with or without fine-tuning, we are curious about whether the instruction-tuned language model is able to achieve well-calibrated results without suffering from the problem of overconfidence (i.e., miscalibration) considering its strong instruction following ability, especially in such limited data setups. In this work, we deliver an in-depth analysis of the behavior across different choices of learning methods from the perspective of both performance and calibration. Through extensive controlled experiments, we observe that the miscalibration problem exists across all learning methods in low-resource setups. To achieve simultaneous gain for both in-task performance and calibration, we then study the potential of self-ensembling applied at different modeling stages (e.g., variations of in-context examples or variations in prompts or different ensembling strategies) to make the predictions more calibrated and have comparable or even better performance. We find that self-ensembling with max probability produces robust and calibrated predictions. Our work reveals the potential calibration problem of using ICL despite the improvements in task performance and sheds light on which learning paradigm to choose. We also provide practical guidelines for choosing learning paradigms depending on whether the data has been seen by the model before and a worthwhile solution via self-ensembling on how to enhance both task performance and calibration of LMs, which we hope could encourage further study.