@inproceedings{jiao-etal-2025-analyzing,
    title = "Analyzing and Modeling {LLM} Response Lengths with Extreme Value Theory: Anchoring Effects and Hybrid Distributions",
    author = "Jiao, Liuxuan  and
      Gao, Chen  and
      Yang, Yiqian  and
      Zhou, Chenliang  and
      Huang, YiXian  and
      Chen, Xinlei  and
      Li, Yong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1676/",
    pages = "32980--32990",
    ISBN = "979-8-89176-332-6",
    abstract = "We present a statistical framework for modeling and controlling large language model (LLM) response lengths using extreme value theory. Analyzing 14,301 GPT-4o responses across temperature and prompting conditions, with cross-validation on Qwen and DeepSeek architectures, we demonstrate that verbosity follows Weibull-type generalized extreme value (GEV) distributions with heavier tails under stochastic generation. Our key contributions include: (1) development of a novel GEV-generalized Pareto (GPD) hybrid model that improves tail fit ($R^2_{\text{CDF}}=0.9993$ vs standalone GEV{'}s 0.998) while maintaining architectural generalizability; (2) quantitative characterization of prompt anchoring effects across models, showing reduced dispersion but increased outliers under randomization; and (3) identification of temperature-dependent response patterns that persist across architectures, with higher temperatures amplifying length variability while preserving extreme-value mechanisms. The hybrid model{'}s threshold selection method enables precise verbosity control in production systems regardless of model choice. While validated on multiple architectures, generalizability to emerging model families requires further study."
}