The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing

• URL: http://arxiv.org/abs/2401.08511v1
• Date: Tue, 16 Jan 2024 17:15:08 GMT
• Title: The Gaps between Pre-train and Downstream Settings in Bias Evaluation and Debiasing
• Authors: Masahiro Kaneko, Danushka Bollegala, Timothy Baldwin
• Abstract summary: In-Context Learning (ICL) induces smaller changes to PLMs compared to FT-based debiasing methods. ICL-based debiasing methods show a higher correlation between intrinsic and extrinsic bias scores compared to FT-based methods.
• Score: 74.7319697510621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The output tendencies of Pre-trained Language Models (PLM) vary markedly before and after Fine-Tuning (FT) due to the updates to the model parameters. These divergences in output tendencies result in a gap in the social biases of PLMs. For example, there exits a low correlation between intrinsic bias scores of a PLM and its extrinsic bias scores under FT-based debiasing methods. Additionally, applying FT-based debiasing methods to a PLM leads to a decline in performance in downstream tasks. On the other hand, PLMs trained on large datasets can learn without parameter updates via In-Context Learning (ICL) using prompts. ICL induces smaller changes to PLMs compared to FT-based debiasing methods. Therefore, we hypothesize that the gap observed in pre-trained and FT models does not hold true for debiasing methods that use ICL. In this study, we demonstrate that ICL-based debiasing methods show a higher correlation between intrinsic and extrinsic bias scores compared to FT-based methods. Moreover, the performance degradation due to debiasing is also lower in the ICL case compared to that in the FT case.

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