Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations
Authors: name: Hakan Inan, name: Kartikeya Upasani, name: Jianfeng Chi, name: Rashi Rungta, name: Krithika Iyer, name: Yuning Mao, name: Michael Tontchev, name: Qing Hu, name: Brian Fuller, name: Davide Testuggine, name: Madian Khabsa

We introduce Llama Guard, an LLM-based input-output safeguard model geared
towards Human-AI conversation use cases. Our model incorporates a safety risk
taxonomy, a valuable tool for categorizing a specific set of safety risks found
in LLM prompts (i.e., prompt classification). This taxonomy is also
instrumental in classifying the responses generated by LLMs to these prompts, a
process we refer to as response classification. For the purpose of both prompt
and response classification, we have meticulously gathered a dataset of high
quality. Llama Guard, a Llama2-7b model that is instruction-tuned on our
collected dataset, albeit low in volume, demonstrates strong performance on
existing benchmarks such as the OpenAI Moderation Evaluation dataset and
ToxicChat, where its performance matches or exceeds that of currently available
content moderation tools. Llama Guard functions as a language model, carrying
out multi-class classification and generating binary decision scores.
Furthermore, the instruction fine-tuning of Llama Guard allows for the
customization of tasks and the adaptation of output formats. This feature
enhances the model's capabilities, such as enabling the adjustment of taxonomy
categories to align with specific use cases, and facilitating zero-shot or
few-shot prompting with diverse taxonomies at the input. We are making Llama
Guard model weights available and we encourage researchers to further develop
and adapt them to meet the evolving needs of the community for AI safety.