@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://preview.aclanthology.org/ingest-ijcnlp-aacl/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."
}