A Dual-Hypothesis Reasoning Framework for LLM Guardrails
Md Asiful Islam, Mihai Surdeanu
Why It Matters
What makes this one worth your time
This framework could improve the safety and interpretability of LLMs, which is crucial for their deployment in sensitive applications.
ARBITER enhances LLM safety by using dual-hypothesis reasoning and efficient fine-tuning.
Summary
The paper introduces ARBITER, a framework for implementing guardrails in large language models (LLMs) using dual-hypothesis reasoning and multi-component supervised fine-tuning. It aims to improve safety moderation by considering both safe and unsafe interpretations of prompts and providing transparent explanations for decisions. The framework is designed to be cost-effective while outperforming existing methods on safety benchmarks.
Key contributions
- Introduction of dual-hypothesis reasoning for LLM guardrails.
- Development of multi-component supervised fine-tuning to decompose and weight LLM outputs.
- Demonstration of cost-effective self-generation strategy for reasoning traces.
Notable insights
- Dual-hypothesis reasoning explicitly considers both safe and unsafe interpretations before making a decision.
- The use of LoRA-based parameter-efficient fine-tuning reduces the cost of implementing guardrails.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.17575v1 Announce Type: new 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.