CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917406256922624 |
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| author | Yang, Jingbo Yao, Guanyu Hou, Bairu Yang, Xinghan Glushnev, Nikolai Bialynicka-Birula, Iwona Ding, Duo Chang, Shiyu |
| author_facet | Yang, Jingbo Yao, Guanyu Hou, Bairu Yang, Xinghan Glushnev, Nikolai Bialynicka-Birula, Iwona Ding, Duo Chang, Shiyu |
| contents | As Large Language Models (LLMs) are increasingly deployed as task-oriented agents in enterprise environments, ensuring their strict adherence to complex, domain-specific operational guidelines is critical. While utilizing an LLM-as-a-Judge is a promising solution for scalable evaluation, the reliability of these judges in detecting specific policy violations remains largely unexplored. This gap is primarily due to the lack of a systematic data generation method, which has been hindered by the extensive cost of fine-grained human annotation and the difficulty of synthesizing realistic agent violations. In this paper, we introduce CompliBench, a novel benchmark designed to evaluate the ability of LLM judges to detect and localize guideline violations in multi-turn dialogues. To overcome data scarcity, we develop a scalable, automated data generation pipeline that simulates user-agent interactions. Our controllable flaw injection process automatically yields precise ground-truth labels for the violated guideline and the exact conversation turn, while an adversarial search method ensures these introduced perturbations are highly challenging. Our comprehensive evaluation reveals that current state-of-the-art proprietary LLMs struggle significantly with this task. In addition, we demonstrate that a small-scale judge model fine-tuned on our synthesized data outperforms leading LLMs and generalizes well to unseen business domains, highlighting our pipeline as an effective foundation for training robust generative reward models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12312 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems Yang, Jingbo Yao, Guanyu Hou, Bairu Yang, Xinghan Glushnev, Nikolai Bialynicka-Birula, Iwona Ding, Duo Chang, Shiyu Computation and Language As Large Language Models (LLMs) are increasingly deployed as task-oriented agents in enterprise environments, ensuring their strict adherence to complex, domain-specific operational guidelines is critical. While utilizing an LLM-as-a-Judge is a promising solution for scalable evaluation, the reliability of these judges in detecting specific policy violations remains largely unexplored. This gap is primarily due to the lack of a systematic data generation method, which has been hindered by the extensive cost of fine-grained human annotation and the difficulty of synthesizing realistic agent violations. In this paper, we introduce CompliBench, a novel benchmark designed to evaluate the ability of LLM judges to detect and localize guideline violations in multi-turn dialogues. To overcome data scarcity, we develop a scalable, automated data generation pipeline that simulates user-agent interactions. Our controllable flaw injection process automatically yields precise ground-truth labels for the violated guideline and the exact conversation turn, while an adversarial search method ensures these introduced perturbations are highly challenging. Our comprehensive evaluation reveals that current state-of-the-art proprietary LLMs struggle significantly with this task. In addition, we demonstrate that a small-scale judge model fine-tuned on our synthesized data outperforms leading LLMs and generalizes well to unseen business domains, highlighting our pipeline as an effective foundation for training robust generative reward models. |
| title | CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.12312 |