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Test & Evaluation

Enable the safety of LLMs.

Our Methodology
    TEST & EVALUATE

    What is Large Language Model Test & Evaluation?

    Continuously test and evaluate LLMs, identify risks, and certify the safety of AI applications.

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    Continuous Evaluation

    Continuously evaluate and monitor the performance of your AI systems.

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    Red Teaming

    Identify severe risks and vulnerabilities for AI systems.

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    AI System Certification

    Forthcoming certifications of AI applications for safety and capability against the latest standards.

    TEST & EVALUATE

    Understand LLM Capabilities, Risks, and Vulnerabilities

    Continuously test and evaluate LLMs, identify risks, and certify the safety of AI applications.

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    Improve Operational Efficiency

    Model Evaluation

    Quantitative and qualitative evaluation of model performance

    Accelerate Speed to Delivery

    Red Teaming

    Identification of model weaknesses and vulnerabilities

    Improve Operational Efficiency

    Automate document processing, inventory management, and intervention workflows with highly accurate AI solutions and tools.

    Accelerate Speed to Delivery

    Upgrade delivery times for your customers with faster decisions, better data from across your supply chain.

    Reduce Compliance Risk

    Accurate data extraction with intelligent document processing reduces exposure to Customs inspections, delays, and fines.

    APPROACH

    Our Approach to Hybrid Test & Evaluation

    We take a hybrid approach to red teaming and model evaluation.

    Hybrid Red Teaming

    Red teaming seeks to elicit undesirable model behavior as a way to assess safety and vulnerabilities. The most effective red teaming pairs automated attack techniques with human experts across a diverse threat surface area.

    Hybrid Model Evaluation

    Continuous model evaluation is critical for assessing model capability and helpfulness over time. A scalable hybrid approach to evaluation leverages LLM-based evaluation combined with human insights where they are most valuable.

    Ecosystem

    An holistic and effective approach to model test and evaluation requires participation and coordination from a broad ecosystem of institutional stakeholders.
    RISKS

    Key Identifiable Risks of LLMs

    Our platform can identify vulnerabilities in multiple categories.

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    Misinformation

    LLMs producing false, misleading, or inaccurate information.

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    Unqualified Advice

    Advice on sensitive topics (i.e. medical, legal, financial) that may result in material harm to the user.

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    Bias

    Responses that reinforce and perpetuate stereotypes that harm specific groups.

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    Privacy

    Disclosing personally identifiable information (PIl) or leaking private data.

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    Cyberattacks

    A malicious actor using a language model to conduct or accelerate a cyberattack.

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    Dangerous Substances

    Assisting bad actors in acquiring or creating dangerous substances or items(e.g. bioweapons, bombs).

    EXPERTS

    Expert Red Teamers

    Scale has a diverse network of experts to perform the LLM evaluation and red teaming to identify risks.

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    Red Team

    Experienced Security & Red Teaming Professionals.

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    Technical

    Coding, STEM, and PhD Experts Across 25+ Other Domains.

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    Defense

    Specialized National Security Expertise.

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    Creatives

    Native English Fluency.

    Red Team

    Experienced Security & Red Teaming Professionals.

    Technical

    Coding, STEM, and PhD Experts Across 25+ Other Domains.

    Defense

    Specialized National Security Expertise.

    Creatives

    Native English Fluency.

    TRUSTED

    Trusted by Federal Agencies and World Class Companies

    Scale was selected to develop the platform to evaluate leading Generative AI Systems at DEFCON 31, as announced by the White House’s Office of Science and Technology Policy (OSTP). We also partner with leading model builders like OpenAI to evaluate the safety of their models.

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    Automated systems should be developed with consultation from diverse communities, stakeholders, and domain experts to identify concerns, risks, and potential impacts of the system. Systems should undergo pre-deployment testing, risk identification and mitigation, and ongoing monitoring that demonstrate they are safe and effective based on their intended use, mitigation of unsafe outcomes including those beyond the intended use, and adherence to domain-specific standards.

    Blueprint for an AI Bill of Rights

    Office of Science and Technology Policy, White House

    OpenAI threw a bunch of tasks at Scale AI with difficult characteristics, including tight latency requirements and significant ambiguity in correct answers. In response, Scale worked closely with us to adjust their QA systems to our needs.

    Geoffrey Irving

    Member of Technical Staff, OpenAI

    Trusted by the world’s most ambitious AI teams.Meet our customers

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