Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop

• URL: http://arxiv.org/abs/2601.05184v1
• Date: Thu, 08 Jan 2026 18:08:15 GMT
• Title: Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop
• Authors: Yaxuan Wang, Zhongteng Cai, Yujia Bao, Xueru Zhang, Yang Liu,
• Abstract summary: We introduce the concept of textbfSelf-textbfConsuming textbfPerformative textbfLoop.<n>We investigate the role of synthetic data in shaping bias during dynamic iterative training processes under controlled performative feedback.
• Score: 17.229330734667474
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid advancement of large language models (LLMs) has led to growing interest in using synthetic data to train future models. However, this creates a self-consuming retraining loop, where models are trained on their own outputs and may cause performance drops and induce emerging biases. In real-world applications, previously deployed LLMs may influence the data they generate, leading to a dynamic system driven by user feedback. For example, if a model continues to underserve users from a group, less query data will be collected from this particular demographic of users. In this study, we introduce the concept of \textbf{S}elf-\textbf{C}onsuming \textbf{P}erformative \textbf{L}oop (\textbf{SCPL}) and investigate the role of synthetic data in shaping bias during these dynamic iterative training processes under controlled performative feedback. This controlled setting is motivated by the inaccessibility of real-world user preference data from dynamic production systems, and enables us to isolate and analyze feedback-driven bias evolution in a principled manner. We focus on two types of loops, including the typical retraining setting and the incremental fine-tuning setting, which is largely underexplored. Through experiments on three real-world tasks, we find that the performative loop increases preference bias and decreases disparate bias. We design a reward-based rejection sampling strategy to mitigate the bias, moving towards more trustworthy self-improving systems.

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