WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models

Summary

This paper introduces WalledEval, a toolkit designed to test the safety of large language models (LLMs) by evaluating how well they handle various safety concerns.

What's the problem?

As large language models are increasingly used in real-world applications, ensuring their safety and reliability is crucial. Current methods for testing these models often lack comprehensiveness and may not adequately assess their ability to handle unsafe or biased content. This raises concerns about the potential harm these models could cause if they generate inappropriate or harmful responses.

What's the solution?

WalledEval provides a comprehensive evaluation framework that includes over 35 safety benchmarks covering different aspects of model behavior, such as multilingual safety and response to harmful prompts. It allows testing of both the models themselves and the judges that evaluate their outputs. The toolkit also introduces new tools like WalledGuard for content moderation and SGXSTest for assessing exaggerated safety in cultural contexts, helping to ensure that models behave safely across various scenarios.

Why it matters?

This research is important because it addresses the critical need for robust safety evaluations of AI systems. By providing a thorough testing toolkit, WalledEval helps developers identify and mitigate risks associated with language models, contributing to the creation of safer AI technologies that can be trusted in sensitive applications.