@inproceedings{wang-etal-2025-bias,
title = "Bias Amplification: Large Language Models as Increasingly Biased Media",
author = "Wang, Ze and
Wu, Zekun and
Zhang, Yichi and
Guan, Xin and
Jain, Navya and
Lu, Qinyang and
Gupta, Saloni and
Koshiyama, Adriano",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.8/",
pages = "115--132",
ISBN = "979-8-89176-298-5",
abstract = "Model collapse{---}a phenomenon where models degrade in performance due to indiscriminate use of synthetic data{---}is well studied. However, its role in bias amplification{---}the progressive reinforcement of pre-existing social biases in Large Language Models (LLMs){---}remains underexplored. In this paper, we formally define the conditions for bias amplification and demonstrate through statistical simulations that bias can intensify even in the absence of sampling errors, the primary driver of model collapse. Empirically, we investigate political bias amplification in GPT-2 using a custom-built benchmark for sentence continuation tasks. Our findings reveal a progressively increasing right-leaning bias. Furthermore, we evaluate three mitigation strategies{---}Overfitting, Preservation, and Accumulation{---}and show that bias amplification persists even when model collapse is mitigated. Finally, a mechanistic interpretation identifies distinct sets of neurons responsible for model collapse and bias amplification, suggesting they arise from different underlying mechanisms."
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<abstract>Model collapse—a phenomenon where models degrade in performance due to indiscriminate use of synthetic data—is well studied. However, its role in bias amplification—the progressive reinforcement of pre-existing social biases in Large Language Models (LLMs)—remains underexplored. In this paper, we formally define the conditions for bias amplification and demonstrate through statistical simulations that bias can intensify even in the absence of sampling errors, the primary driver of model collapse. Empirically, we investigate political bias amplification in GPT-2 using a custom-built benchmark for sentence continuation tasks. Our findings reveal a progressively increasing right-leaning bias. Furthermore, we evaluate three mitigation strategies—Overfitting, Preservation, and Accumulation—and show that bias amplification persists even when model collapse is mitigated. Finally, a mechanistic interpretation identifies distinct sets of neurons responsible for model collapse and bias amplification, suggesting they arise from different underlying mechanisms.</abstract>
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%0 Conference Proceedings
%T Bias Amplification: Large Language Models as Increasingly Biased Media
%A Wang, Ze
%A Wu, Zekun
%A Zhang, Yichi
%A Guan, Xin
%A Jain, Navya
%A Lu, Qinyang
%A Gupta, Saloni
%A Koshiyama, Adriano
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F wang-etal-2025-bias
%X Model collapse—a phenomenon where models degrade in performance due to indiscriminate use of synthetic data—is well studied. However, its role in bias amplification—the progressive reinforcement of pre-existing social biases in Large Language Models (LLMs)—remains underexplored. In this paper, we formally define the conditions for bias amplification and demonstrate through statistical simulations that bias can intensify even in the absence of sampling errors, the primary driver of model collapse. Empirically, we investigate political bias amplification in GPT-2 using a custom-built benchmark for sentence continuation tasks. Our findings reveal a progressively increasing right-leaning bias. Furthermore, we evaluate three mitigation strategies—Overfitting, Preservation, and Accumulation—and show that bias amplification persists even when model collapse is mitigated. Finally, a mechanistic interpretation identifies distinct sets of neurons responsible for model collapse and bias amplification, suggesting they arise from different underlying mechanisms.
%U https://aclanthology.org/2025.ijcnlp-long.8/
%P 115-132
Markdown (Informal)
[Bias Amplification: Large Language Models as Increasingly Biased Media](https://aclanthology.org/2025.ijcnlp-long.8/) (Wang et al., IJCNLP-AACL 2025)
ACL
- Ze Wang, Zekun Wu, Yichi Zhang, Xin Guan, Navya Jain, Qinyang Lu, Saloni Gupta, and Adriano Koshiyama. 2025. Bias Amplification: Large Language Models as Increasingly Biased Media. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 115–132, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.