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Prompting and in-context learning (ICL) have become efficient learningparadigms for large language models (LLMs). However, LLMs suffer from promptbrittleness and various bias factors in the prompt, including but not limitedto the formatting, the choice verbalizers, and the ICL examples. To addressthis problem that results in unexpected performance degradation, calibrationmethods have been developed to mitigate the effects of these biases whilerecovering LLM performance. In this work, we first conduct a systematicanalysis of the existing calibration methods, where we both provide a unifiedview and reveal the failure cases. Inspired by these analyses, we propose BatchCalibration (BC), a simple yet intuitive method that controls the contextualbias from the batched input, unifies various prior approaches, and effectivelyaddresses the aforementioned issues. BC is zero-shot, inference-only, andincurs negligible additional costs. In the few-shot setup, we further extend BCto allow it to learn the contextual bias from labeled data. We validate theeffectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstratestate-of-the-art performance over previous calibration baselines across morethan 10 natural language understanding and image classification tasks.

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering