arXiv：cs.LG· Petmongkon Pornpichitsuwan, Krittin Pachtrachai, Touchapon Kraisingkorn·· 5 小时前
用 FlowSpec 训练语音智能体：让催收对话迁移到未见过的合规流程
Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection
AI 导读
研究者提出 FlowSpec，用每套部署一份的机器可读规范生成策略指令、门控工具、提供监督并定义测试，让智能体只说经批准的目录话术。基于 297 套随机流程的 2,506 段对话微调 9B 参数模型后，泰国催收外呼智能体在四套未见流程上把流程固定检查项从 67–71% 提升至 93–100%，场景通过率从 15/43 升至 40/43。
正文
Abstract:Voice agents built on large language models (LLMs) now handle customer calls, but in regulated applications completing the task is not enough: the agent must follow a conversational flow set by law. In debt collection, it must confirm it is speaking to the debtor before mentioning the debt, identify itself and the creditor, state the amount, and avoid prohibited language. Every creditor turns these rules into its own procedure and revises it. We ask whether a small, self-hosted policy can be trained once and then follow regulated procedures it has never seen. One machine-readable specification per deployment, FlowSpec, generates the policy instruction, gates the tools, supplies the supervision and defines the tests; the agent speaks only approved catalogue sentences. We fine-tune a 9-billion-parameter model on 2,506 conversations from 297 randomized procedures and evaluate a Thai outbound-call agent on four unseen deployment procedures (three debt collection, one non-debt), one from a wholly withheld lineage. Documented checks, fixed by the procedure, rise from 67--71 percents to 93--100 percents, while grounded checks, fixed by the conversation, already pass at 97--100 percents before training; scenario pass rate rises from 15/43 to 40/43. An audit of the shared specification finds 11 defects, 3 of which its own metric did not expose.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.18881 [cs.CL] |
| (or arXiv:2610.18881v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18881 arXiv-issued DOI via DataCite |
Submission history
From: Krittin Pachtrachai PhD [view email]
[v1]
Thu, 1 Oct 2026 03:08:52 UTC (74 KB)
来源：arXiv：cs.LG · arxiv.org