Academic

ExpGuard: LLM Content Moderation in Specialized Domains

arXiv:2603.02588v1 Announce Type: new Abstract: With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their inputs and outputs has become essential to ensure adherence to safety policies. Current guardrail models predominantly address general human-LLM interactions, rendering LLMs vulnerable to harmful and adversarial content within domain-specific contexts, particularly those rich in technical jargon and specialized concepts. To address this limitation, we introduce ExpGuard, a robust and specialized guardrail model designed to protect against harmful prompts and responses across financial, medical, and legal domains. In addition, we present ExpGuardMix, a meticulously curated dataset comprising 58,928 labeled prompts paired with corresponding refusal and compliant responses, from these specific sectors. This dataset is divided into two subsets: ExpGuardTrain, for model training, and ExpGuardTest, a h

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Minseok Choi, Dongjin Kim, Seungbin Yang, Subin Kim, Youngjun Kwak, Juyoung Oh, Jaegul Choo, Jungmin Son · · 1 min read · 2 views
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arXiv:2603.02588v1 Announce Type: new Abstract: With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety guardrails to moderate their inputs and outputs has become essential to ensure adherence to safety policies. Current guardrail models predominantly address general human-LLM interactions, rendering LLMs vulnerable to harmful and adversarial content within domain-specific contexts, particularly those rich in technical jargon and specialized concepts. To address this limitation, we introduce ExpGuard, a robust and specialized guardrail model designed to protect against harmful prompts and responses across financial, medical, and legal domains. In addition, we present ExpGuardMix, a meticulously curated dataset comprising 58,928 labeled prompts paired with corresponding refusal and compliant responses, from these specific sectors. This dataset is divided into two subsets: ExpGuardTrain, for model training, and ExpGuardTest, a high-quality test set annotated by domain experts to evaluate model robustness against technical and domain-specific content. Comprehensive evaluations conducted on ExpGuardTest and eight established public benchmarks reveal that ExpGuard delivers competitive performance across the board while demonstrating exceptional resilience to domain-specific adversarial attacks, surpassing state-of-the-art models such as WildGuard by up to 8.9% in prompt classification and 15.3% in response classification. To encourage further research and development, we open-source our code, data, and model, enabling adaptation to additional domains and supporting the creation of increasingly robust guardrail models.

Executive Summary

This article proposes ExpGuard, a specialized guardrail model designed to moderate large language models (LLMs) in domain-specific contexts, such as finance, medicine, and law. ExpGuard demonstrates exceptional resilience to domain-specific adversarial attacks, outperforming state-of-the-art models by up to 15.3% in response classification. The model's performance is evaluated on a curated dataset, ExpGuardTest, and eight public benchmarks. The authors open-source their code, data, and model, facilitating further research and adaptation to additional domains. This development is crucial in ensuring the safety and integrity of LLMs in sensitive fields.

Key Points

  • ▸ ExpGuard is a specialized guardrail model for domain-specific LLM content moderation
  • ▸ The model demonstrates exceptional resilience to domain-specific adversarial attacks
  • ▸ ExpGuard outperforms state-of-the-art models by up to 15.3% in response classification

Merits

Strengths of ExpGuard's Architecture

ExpGuard's modular design and adaptability to various domains make it a robust solution for LLM content moderation

Exceptional Performance in Adversarial Attacks

ExpGuard's ability to outperform state-of-the-art models in response classification demonstrates its effectiveness in protecting against domain-specific threats

Open-Source Availability

The authors' decision to open-source their code, data, and model facilitates further research and adaptation, accelerating the development of more robust guardrail models

Demerits

Limited Domain Scope

ExpGuard's current focus on finance, medicine, and law domains may limit its applicability to other specialized fields

Dataset Size and Quality

While the ExpGuardTest dataset is meticulously curated, its size and quality may impact model performance and adaptability to new domains

Expert Commentary

The introduction of ExpGuard marks a significant step towards ensuring the safety and integrity of LLMs in sensitive fields. However, its limitations and potential challenges highlight the need for continued research and development in AI safety and governance. As the authors note, the open-source availability of ExpGuard facilitates further research and adaptation, accelerating the creation of more robust guardrail models. Nevertheless, the practical and policy implications of ExpGuard's implementation cannot be overstated, requiring careful consideration and investment to ensure its effectiveness in real-world applications.

Recommendations

  • ✓ Researchers and developers should prioritize the adaptation of ExpGuard to various domains, building on its modular design and adaptability
  • ✓ Governments and regulatory bodies should revise their policies on LLM content moderation to accommodate the unique requirements of domain-specific models like ExpGuard

Sources

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