WildGuard: A Light-weight, Multi-Purpose Moderation Tool for Assessing the Safety of User-LLM Interactions
How do existing moderation tools like Llama-Guard function?
Existing moderation tools like Llama-Guard function by performing multi-class classification and generating binary decision scores5. They are designed to act as safeguards in human-AI conversations, addressing the need for content moderation in the era of large language models6. These tools demonstrate strong performance on benchmarks like the OpenAI Moderation Evaluation dataset and ToxicChat, often outperforming other available content moderation tools.
How is the WILDGUARDTEST dataset structured?
The WILDGUARDTEST dataset is a high-quality, human-annotated evaluation set containing 5,299 items. It covers a diverse range of risk scenarios, including both direct and adversarial prompts, paired with refusal and compliance responses. This dataset is part of the larger WILDGUARDMIX dataset, specifically designed for evaluating the performance of the WILDGUARD moderation tool in open-source safety moderation tasks1.
What are the main risks of unmoderated LLM interactions?
Unmoderated LLM interactions pose several risks, including the generation of harmful content, successful adversarial attacks (jailbreaks), and inadequate refusal of inappropriate requests. These risks can lead to the spread of misinformation, biased outputs, and privacy violations, making moderation essential for maintaining trust and applicability in sensitive domains like healthcare, finance, and social media.