Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences

• URL: http://arxiv.org/abs/2501.06795v1
• Date: Sun, 12 Jan 2025 12:32:43 GMT
• Title: Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences
• Authors: Liu Yu, Ludie Guo, Ping Kuang, Fan Zhou,
• Abstract summary: We propose enhancing fairness (Fair-Gender) in pre-trained language models (PLMs) by absorbing coherent, attribute-balanced, and semantically rich sentences. These sentences cannot be directly used for debiasing due to alignment issues and the risk of negative transfer. We address this by applying causal analysis to estimate causal effects, filtering out unaligned sentences, and identifying aligned ones for incorporation into PLMs.
• Score: 8.979854959662664
• License:
• Abstract: Pre-trained language models (PLMs) are trained on data that inherently contains gender biases, leading to undesirable impacts. Traditional debiasing methods often rely on external corpora, which may lack quality, diversity, or demographic balance, affecting the effectiveness of debiasing. With the rise of large language models and their extensive knowledge, we propose enhancing fairness (Fair-Gender) in PLMs by absorbing coherent, attribute-balanced, and semantically rich sentences. However, these sentences cannot be directly used for debiasing due to alignment issues and the risk of negative transfer. We address this by applying causal analysis to estimate causal effects, filtering out unaligned sentences, and identifying aligned ones for incorporation into PLMs, thereby ensuring positive transfer. Experiments show that our approach significantly reduces gender biases in PLMs while preserving their language expressiveness.

Related papers

• Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection [5.800102484016876]
  Large Language Models (LLMs) have been shown to exhibit various biases and stereotypes in their generated content. This paper presents a systematic framework grounded in social psychology theories to investigate explicit and implicit biases in LLMs.
  arXiv  Detail & Related papers  (2025-01-04T14:08:52Z)
• Towards Resource Efficient and Interpretable Bias Mitigation in Large Language Models [1.787433808079955]
  Large language models (LLMs) have been observed to perpetuate unwanted biases in training data. In this paper, we mitigate bias by leveraging small biased and anti-biased expert models to obtain a debiasing signal. Experiments on mitigating gender, race, and religion biases show a reduction in bias on several local and global bias metrics.
  arXiv  Detail & Related papers  (2024-12-02T16:56:08Z)
• How far can bias go? -- Tracing bias from pretraining data to alignment [54.51310112013655]
  This study examines the correlation between gender-occupation bias in pre-training data and their manifestation in LLMs. Our findings reveal that biases present in pre-training data are amplified in model outputs.
  arXiv  Detail & Related papers  (2024-11-28T16:20:25Z)
• The Root Shapes the Fruit: On the Persistence of Gender-Exclusive Harms in Aligned Language Models [58.130894823145205]
  We center transgender, nonbinary, and other gender-diverse identities to investigate how alignment procedures interact with pre-existing gender-diverse bias. Our findings reveal that DPO-aligned models are particularly sensitive to supervised finetuning. We conclude with recommendations tailored to DPO and broader alignment practices.
  arXiv  Detail & Related papers  (2024-11-06T06:50:50Z)
• A Novel Interpretability Metric for Explaining Bias in Language Models: Applications on Multilingual Models from Southeast Asia [0.3376269351435396]
  We propose a novel metric to measure token-level contributions to biased behavior in pretrained language models (PLMs) Our results confirm the presence of sexist and homophobic bias in Southeast Asian PLMs. Interpretability and semantic analyses also reveal that PLM bias is strongly induced by words relating to crime, intimate relationships, and helping.
  arXiv  Detail & Related papers  (2024-10-20T18:31:05Z)
• Towards Transfer Unlearning: Empirical Evidence of Cross-Domain Bias Mitigation [18.150899267807965]
  We study an unlearning-based approach to debiasing in large language models (LLMs) We propose a mask language modeling unlearning technique, which unlearns the harmful part of the text. Experimental results demonstrate the effectiveness of our approach in diminishing bias while maintaining the language modeling abilities.
  arXiv  Detail & Related papers  (2024-07-24T02:37:42Z)
• The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models [78.69526166193236]
  Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases. We propose sc Social Bias Neurons to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost.
  arXiv  Detail & Related papers  (2024-06-14T15:41:06Z)
• The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated [70.23064111640132]
  We compare the impact of debiasing on performance across multiple downstream tasks using a wide-range of benchmark datasets. Experiments show that the effects of debiasing are consistently emphunderestimated across all tasks.
  arXiv  Detail & Related papers  (2023-09-16T20:25:34Z)
• In-Contextual Gender Bias Suppression for Large Language Models [47.246504807946884]
  Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. We propose bias suppression that prevents biased generations of LLMs by providing preambles constructed from manually designed templates. We find that bias suppression has acceptable adverse effect on downstream task performance with HellaSwag and COPA.
  arXiv  Detail & Related papers  (2023-09-13T18:39:08Z)
• Balancing out Bias: Achieving Fairness Through Training Reweighting [58.201275105195485]
  Bias in natural language processing arises from models learning characteristics of the author such as gender and race. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. This paper introduces a very simple but highly effective method for countering bias using instance reweighting.
  arXiv  Detail & Related papers  (2021-09-16T23:40:28Z)
• Towards Understanding and Mitigating Social Biases in Language Models [107.82654101403264]
  Large-scale pretrained language models (LMs) can be potentially dangerous in manifesting undesirable representational biases. We propose steps towards mitigating social biases during text generation. Our empirical results and human evaluation demonstrate effectiveness in mitigating bias while retaining crucial contextual information.
  arXiv  Detail & Related papers  (2021-06-24T17:52:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.