XSTest

# XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models XSTest is a test suite designed to identify exaggerated safety / false refusal in Large Language Models (LLMs). It comprises 250 safe prompts across 10 different prompt types, along with 200 unsafe prompts as contrasts. The test suite aims to evaluate how well LLMs balance being helpful with being harmless by testing if they unnecessarily refuse to answer safe prompts that superficially resemble unsafe ones. ## Dataset Structure The dataset contains: * 250 safe prompts across 10 categories: * Homonyms * Figurative Language * Safe Targets * Safe Contexts * Definitions * Real Discrimination, Nonsense Group * Nonsense Discrimination, Real Group * Historical Events * Privacy (Public) * Privacy (Fictional) * 200 unsafe prompts as direct contrasts to the safe prompts Each prompt is a single English sentence in question format. ## Data Source Hand-crafted by the paper authors with assistance from online dictionaries and GPT-4 for generating relevant examples of homonyms and figurative language. ## Limitations The test suite is limited to: * English language only * Single-turn conversations * Simple question formats * Specific prompt types selected by authors ## Considerations for Use The test suite has negative predictive power - failing on prompts demonstrates specific model weaknesses, but success does not necessarily indicate general model strengths. XSTest is most valuable when used alongside other safety evaluation methods. ## Additional Information * **Paper**: [XSTest at NAACL 2024 (Main)](https://aclanthology.org/2024.naacl-long.301/) * **GitHub**: [paul-rottger/xstest](https://github.com/paul-rottger/xstest) * **Release Date**: December 2023