Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models
Kang He, Yinghan Long, Kaushik Roy · 2024
Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings.In this work, we propose a null-input prompting method to calibrate intrinsic bias encoded in pre-trained LMs.Different from prior efforts that address intrinsic bias primarily for social fairness and often involve excessive computational cost, our objective is to explore enhancing LMs' performance in downstream zero/fewshot learning while emphasizing the efficiency of intrinsic bias calibration.Specifically, we leverage a diverse set of auto-selected nullmeaning inputs generated from GPT-4 to probe intrinsic bias of pre-trained LMs.Utilizing the bias-reflected probability distribution, we formulate a distribution disparity loss for bias calibration, where we exclusively update bias parameters (0.1% of total parameters) of LMs towards equal probability distribution.Experimental results show that the calibration promotes an equitable starting point for LMs while preserving language modeling abilities.Across a wide range of datasets, including sentiment analysis and topic classification, our method significantly improves zero/few-shot learning performance of LMs for both in-context learning and prompt-based fine-tuning (on average 9% and 2%, respectively). 1