MaskLLM

MaskLLM functions as a public, community-driven archive dedicated to cataloging documented breakdowns in large language models. Key aspects include: * Detailed records of model hallucinations and reasoning errors * Examples of sycophantic responses and security circumventions * Structured submissions for dataset export * Support for interpretability research and red-team evaluations This platform collects comprehensive accounts of how language models can deviate from expected behavior. Each submission captures the exact prompt and problematic response, categorized by failure type and severity, alongside a description of the issue's significance. This systematic approach ensures that the data is readily usable for technical analysis. The archive serves as a critical resource for researchers and developers working to understand and mitigate model limitations. By centralizing failure modes, MaskLLM facilitates a deeper investigation into the underlying mechanisms of these systems, promoting robustness and reliability. Ideal for engineers, ethicists, and researchers focused on improving language model performance, identifying vulnerabilities, and advancing the understanding of their operational boundaries.