@inproceedings{liu-etal-2026-fair,
title = "Fair-{CCD}: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding",
author = "Liu, Donghan and
Sun, Han and
Wang, Zhaohui and
Li, Qin and
Zhang, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1317/",
pages = "28561--28575",
ISBN = "979-8-89176-390-6",
abstract = "While recent studies show the effectiveness of in-context learning (ICL) for tabular data prediction, they also reveal significant fairness issues in large language models (LLMs). Prior work to mitigate fairness issues often employs interventions relying on subjective demonstration selection. Its effectiveness varies significantly with the specific demonstration content, leading to low controllability. Moreover, the improvement of fairness is highly unstable across different models and tasks. To address the challenges of low controllability and limited stability in fairness interventions, we propose Fairness-Aware Context-Contrastive Decoding (Fair-CCD). Fair-CCD first constructs Structural Bias Templates (SBTs), motivated by behavioral patterns observed in demonstrations, to encode the relationship between sensitive attributes and predicted labels in a structured and controllable form. During inference, Fair-CCD injects multiple SBTs and contrasts the model{'}s responses, generating two differential signals that guide fairness adjustment and preserve task performance. By leveraging attention signals to scale decoding adjustments guided by the difference signals, Fair-CCD achieves stable and adaptive bias mitigation across models and tasks. Extensive experimental results demonstrate that Fair-CCD consistently improves fairness metrics without degrading task accuracy."
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<abstract>While recent studies show the effectiveness of in-context learning (ICL) for tabular data prediction, they also reveal significant fairness issues in large language models (LLMs). Prior work to mitigate fairness issues often employs interventions relying on subjective demonstration selection. Its effectiveness varies significantly with the specific demonstration content, leading to low controllability. Moreover, the improvement of fairness is highly unstable across different models and tasks. To address the challenges of low controllability and limited stability in fairness interventions, we propose Fairness-Aware Context-Contrastive Decoding (Fair-CCD). Fair-CCD first constructs Structural Bias Templates (SBTs), motivated by behavioral patterns observed in demonstrations, to encode the relationship between sensitive attributes and predicted labels in a structured and controllable form. During inference, Fair-CCD injects multiple SBTs and contrasts the model’s responses, generating two differential signals that guide fairness adjustment and preserve task performance. By leveraging attention signals to scale decoding adjustments guided by the difference signals, Fair-CCD achieves stable and adaptive bias mitigation across models and tasks. Extensive experimental results demonstrate that Fair-CCD consistently improves fairness metrics without degrading task accuracy.</abstract>
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%0 Conference Proceedings
%T Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding
%A Liu, Donghan
%A Sun, Han
%A Wang, Zhaohui
%A Li, Qin
%A Zhang, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-fair
%X While recent studies show the effectiveness of in-context learning (ICL) for tabular data prediction, they also reveal significant fairness issues in large language models (LLMs). Prior work to mitigate fairness issues often employs interventions relying on subjective demonstration selection. Its effectiveness varies significantly with the specific demonstration content, leading to low controllability. Moreover, the improvement of fairness is highly unstable across different models and tasks. To address the challenges of low controllability and limited stability in fairness interventions, we propose Fairness-Aware Context-Contrastive Decoding (Fair-CCD). Fair-CCD first constructs Structural Bias Templates (SBTs), motivated by behavioral patterns observed in demonstrations, to encode the relationship between sensitive attributes and predicted labels in a structured and controllable form. During inference, Fair-CCD injects multiple SBTs and contrasts the model’s responses, generating two differential signals that guide fairness adjustment and preserve task performance. By leveraging attention signals to scale decoding adjustments guided by the difference signals, Fair-CCD achieves stable and adaptive bias mitigation across models and tasks. Extensive experimental results demonstrate that Fair-CCD consistently improves fairness metrics without degrading task accuracy.
%U https://aclanthology.org/2026.acl-long.1317/
%P 28561-28575
Markdown (Informal)
[Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding](https://aclanthology.org/2026.acl-long.1317/) (Liu et al., ACL 2026)
ACL