EVALUATION CHALLENGES
Lack of high quality, trustworthy evaluation datasets (which have not been overfit on).
Lack of good product tooling for understanding and iterating on evaluation results.
Lack of consistency in model comparisons and reliability in reporting.
WHY SLYMELAB
AI Evals is designed to enable frontier model developers to understand, analyze, and iterate on their models by providing detailed breakdowns of LLMs across multiple facets of performance and safety.
High-quality evaluation sets across domains and capabilities ensure accurate model assessments without overfitting.
User-friendly interface for analyzing and reporting on model performance across domains, capabilities, and versioning.
Custom evaluation sets focus on specific model concerns, enabling precise improvements via new training data.
Expert human raters provide reliable evaluations, backed by transparent metrics and quality assurance mechanisms.
Enables standardized model evaluations for true apples-to-apples comparisons across models.
RISKS
Our platform can identify vulnerabilities in multiple categories.
LLMs producing false, misleading, or inaccurate information.
Advice on sensitive topics (i.e. medical, legal, financial) that may result in material harm.
Responses that reinforce and perpetuate stereotypes that harm specific groups.
Disclosing personally identifiable information (PII) or leaking private data.
A malicious actor using a language model to conduct or accelerate a cyberattack.
Assisting bad actors in acquiring or creating dangerous substances or items.
EXPERTS
SlymeLab has a diverse network of experts to perform the LLM evaluation and red teaming to identify risks.
TECHNIQUES
Stylized input in prompt
Fictionalization & role-play
Encoded input in prompt
Dialog injection
HARMS
Cybersecurity & hacking
Promotion of violence
Dangerous substances & items
Misrepresentation
1000s of red teamers trained on advanced tactics and in-house prompt engineers enable state of the art red teaming at scale.
Extensive libraries and taxonomies of tactics and harms ensure broad coverage of vulnerability areas.
Proprietary adversarial prompt sets are used to conduct systematic model vulnerability scans.
Continuous monitoring of AI-safety developments ensures evaluation methodology remains current.
Active tracking of emerging AI regulations to keep evaluation frameworks aligned with compliance requirements.
"The work SlymeLab is doing to evaluate the performance, reliability, and safety of AI models is crucial. Government agencies and the general public alike need an independent, third party like SlymeLab to have confidence that AI systems are trustworthy and to accelerate responsible AI development."
Dr. Craig Martell
Former Chief Digital and Artificial Intelligence Officer (CDAO), U.S. Department of Defense
RESOURCES