[2603.13683] Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation

Computer Science > Computation and Language arXiv:2603.13683 (cs) [Submitted on 14 Mar 2026 (v1), last revised 16 Apr 2026 (this version, v2)] Title:Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation Authors:Hanwen Shen, Ting Ying, Jiajie Lu, Shanshan Wang View a PDF of the paper titled Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation, by Hanwen Shen and 3 other authors View PDF HTML (experimental) Abstract:Although debiased large language models (LLMs) excel at handling known or low-bias prompts, they often fail on unfamiliar and high-bias prompts. We demonstrate via out-of-distribution (OOD) detection that these high-bias prompts cause a distribution shift, degrading static model performance. To enable real-time correction, we propose CAP-TTA, a test-time adaptation framework. CAP-TTA triggers context-aware LoRA updates only when a bias-risk score exceeds a set threshold. By utilizing an offline precomputed diagonal preconditioner, it ensures fast and stable optimization. Across multiple benchmarks and human evaluations, CAP-TTA effectively reduces toxicity/bias score with significantly lower latency than standard optimization methods (e.g., AdamW or SGD). Furthermore, it prevents catastrophic forgetting, and substantially improves narrative fluency over state-of-the-art baselines without compromising debiasing performance. Comments: Subjects: Computation and Language (cs.CL); Artific...