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Cost-Aware Abstention for LLM-Based IT Ticket Classification

Sachin Gupta · 2026 · arXiv

DOI: 10.48550/arXiv.2610.31657 · open-access PDF

Chinese

错误的IT支持票的自动路由可能比人类分类成本更高。我们评估成本意识的弃权作为离线、成本条件的准备评估:在明确成本下的校准数据上选择路由策略,然后评估冻结的政策一次在持有测试数据上。在公开的47837票,八类的体积上,我们将四个训练有素的非LLM分类器与四个大型语言模型进行对比,以零和少量提示。

Machine translation (Meta M2M100); the English original follows.

Abstract

Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on calibration data under explicit costs, then evaluate that frozen policy once on held-out test data. On a public 47,837-ticket, eight-class corpus, we compare four trained non-LLM classifiers with four large language models under zero- and few-shot prompting. Under the illustrative cost model (c_ok, c_triage, c_wrong) = (0, 1, 5), TF-IDF with logistic regression and a threshold of 0.60 selected on cali

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