BingoGuard: LLM Content Moderation Tools with Risk Levels
Keywords: LLM, safety guardrail, content moderator
TL;DR: We build a LLM moderator that can express risk levels. This moderator achieves the best performance on public benchmarks and our new test set.
Abstract: Malicious content generated by large language models (LLMs) can pose varying degrees of harm.
Although existing LLM-based moderators can detect harmful content, they struggle to assess risk levels and may miss lower-risk outputs.
Accurate risk assessment allows platforms with different safety thresholds to tailor content filtering and rejection. In this paper, we introduce per-topic severity rubrics for 11 harmful topics and build BingoGuard, an LLM-based moderation system designed to predict both binary safety labels and severity levels.
To address the lack of annotations on levels of severity, we propose a scalable generate-then-filter framework that first generates responses across different severity levels and then filters out low-quality responses. Using this framework, we create BingoGuardTrain, a training dataset with 54,897 examples covering a variety of topics, response severity, styles, and BingoGuardTest, a test set with 988 examples explicitly labeled based on our severity rubrics that enables fine-grained analysis on model behaviors on different severity levels. Our BingoGuard-8B, trained on BingoGuardTrain, achieves the state-of-the-art performance on several moderation benchmarks, including WildGuardTest and HarmBench, as well as BingoGuardTest, outperforming best public models, WildGuard, by 4.3\%. Our analysis demonstrates that incorporating severity levels into training significantly enhances detection performance and enables the model to effectively gauge the severity of harmful responses. Warning: this paper includes red-teaming examples that may be harmful in nature.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 12595
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