FinVector-Market-4B: A Controlled Study of LoRA Adaptation for Structured Financial Tasks
Summary
本文提出 FinVector-Market-4B，使用 rank-16 LoRA 在 22,000 条语料上对 Qwen3.5-4B 进行结构化金融任务的适配研究，表明在匹配提示契约下，紧凑型领域适配能带来超出输出格式学习的显著任务收益。
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# FinVector-Market-4B: A Controlled Study of LoRA Adaptation for Structured Financial Tasks Source: [https://arxiv.org/abs/2610.08882](https://arxiv.org/abs/2610.08882) [View PDF](https://arxiv.org/pdf/2610.08882) > Abstract:FinVector\-Market\-4B adapts Qwen/Qwen3\.5\-4B with rank\-16 LoRA on a 22,000\-example corpus for structured financial tasks\. We evaluate the base and adapted models on the same 600\-example benchmark under implicit and explicit JSON\-schema contracts\. Supplying the schema alone raises base\-model JSON validity from 0% to 91\.3%\. Under matched explicit prompting, the frozen scores improve from 14\.7% to 40\.0% for FinQA answer exact match, from 48\.0% to 82\.7% for calculator\-expression correctness, from 20\.1% to 89\.5% for scenario branch\-label agreement, and from 52\.4% to 87\.2% for implication\-direction agreement\. A post\-hoc policy\-scoring audit shows that the reported macro\-F1 decline reflects a changing label set; using the same three target classes gives 77\.4% for the base and 83\.1% for the adapter\. Filing overlap and calculator\-target inconsistencies qualify the benchmark's generalization claims\. The results show that compact financial domain adaptation can produce substantial task\-specific gains beyond output\-format learning under matched prompting, with gains bounded by the evaluated task distribution and prompt contract\. ## Submission history From: Alina Khaybullina \[[view email](https://arxiv.org/show-email/8049f920/2610.08882)\] **\[v1\]**Tue, 6 Oct 2026 10:46:58 UTC \(567 KB\)
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