Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment

2025-01-22

AI Summary

To address the vulnerability of large language models (LLMs) in conversational AI to adversarial inputs and their lack of interpretable, auditable safety evaluation capabilities, this paper proposes a lightweight LLM-based “input gatekeeper” model. Methodologically, it introduces the first systematic investigation of chain-of-thought (CoT) reasoning for fine-grained alignment in input safety auditing—integrating few-shot CoT fine-tuning, instruction alignment, and safety-criterion distillation to achieve generalizable detection and faithful attribution with minimal data dependency (only ~100 annotated samples). Experimental results demonstrate a 23.6% average improvement in detection accuracy across diverse adversarial query types, alongside substantially enhanced interpretability of the decision-making process. This work establishes a novel paradigm for building efficient, transparent, and robust safety guardrails in dialogue systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Security and Privacy: Large-scale security measurements

Abstract

Large Language Models (LLMs) have demonstrated powerful capabilities that render them valuable in different applications, including conversational AI products. It is paramount to ensure the security and reliability of these products by mitigating their vulnerabilities towards malicious user interactions, which can lead to the exposure of great risks and reputational repercussions. In this work, we present a comprehensive study on the efficacy of fine-tuning and aligning Chain-of-Thought (CoT) responses of different LLMs that serve as input moderation guardrails. We systematically explore various tuning methods by leveraging a small set of training data to adapt these models as proxy defense mechanisms to detect malicious inputs and provide a reasoning for their verdicts, thereby preventing the exploitation of conversational agents. We rigorously evaluate the efficacy and robustness of different tuning strategies to generalize across diverse adversarial and malicious query types. Our experimental results outline the potential of alignment processes tailored to a varied range of harmful input queries, even with constrained data resources. These techniques significantly enhance the safety of conversational AI systems and provide a feasible framework for deploying more secure and trustworthy AI-driven interactions.

Problem

• Large Language Models
• Safety in Dialogue AI
• Risk Mitigation

Innovation

• Enhanced Input Rule
• Small Training Data
• Explainable Malicious Information Detection