arXiv cs.AI· Md Asiful Islam, Fahmida Alam, Mihai Surdeanu·· 9 天前AI 评分22
ARBITER：面向 LLM 护栏的双假设推理框架
A Dual-Hypothesis Reasoning Framework for LLM Guardrails
AI 导读
研究者提出 LLM 护栏框架 ARBITER，采用双假设推理，在做出安全判定前同时考虑提示词的安全与不安全解读，并用多组件监督微调（MC-SFT）将输出拆解为逻辑组件按重要性加权。该框架以自生成推理轨迹和 LoRA 参数高效微调替代昂贵的大模型教师蒸馏与全参数微调，在三个安全审核基准上优于现有推理型与非推理型护栏基线，且跨域评测提升明显。
正文 · 原文
Abstract:We propose ARBITER, a novel LLM guardrail framework that introduces two key ideas: (i) dual-hypothesis reasoning, a reasoning method for LLM guardrails that explicitly considers both safe and unsafe interpretations of a prompt before making a safety decision, and (ii) multi-component supervised fine-tuning (MC-SFT), a structured training loss for reasoning-based guardrails that decomposes LLM outputs into logical components and weights them according to their importance. Existing reasoning-based guardrails often rely on expensive procedures, such as generating reasoning traces using larger or closed-source teacher models and applying full-parameter fine-tuning. In contrast, ARBITER uses a cost-effective self-generation strategy for reasoning traces and LoRA-based parameter-efficient fine-tuning while still achieving better performance than these expensive approaches. Additionally, ARBITER provides faithful evidence-phrase explanations for unsafe decisions, enabling a more transparent and interpretable guardrail method. Experiments on three safety moderation benchmarks show that ARBITER outperforms existing reasoning-based and non-reasoning guardrail baselines, with clear gains in out-of-domain evaluations.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.17575 [cs.AI] |
| (or arXiv:2607.17575v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.17575 arXiv-issued DOI via DataCite |
Submission history
From: Md Asiful Islam [view email]
[v1]
Mon, 20 Jul 2026 05:42:25 UTC (1,688 KB)
[v2]
Mon, 5 Oct 2026 03:32:38 UTC (1,708 KB)
[v3]
Wed, 7 Oct 2026 17:23:11 UTC (1,708 KB)
来源：arXiv cs.AI · arxiv.org