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Safely launch your
LLM based chatbot

Large Language Models tuned with your own data hold great promise for customer support, internal efficiency, and new use cases. A specific case are Retrieval Augmented Generation systems (RAG) where the LLM output is based on a search result, reducing hallucinations and improving explainability. Calvin's independent validation of RAGs and other LLM solutions speeds up decision making, drives quality, and avoids reputational or other losses.

Explore LLM Assessment Features

LLM Performance and Robustness

Calculate 6 performance and 11 robustness metrics to understand the technical quality of your LLM.

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LLM Fairness and Explainability

Analyze bias using 2 fairness metrics. Enhance explainability using pre-trained classifiers on intermediate results.

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LLM Safety and Accountability

Estimate regulatory risk dimensions based on governance and compliance status according to the Calvin methodology.

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How can Calvin Risk help ensure my LLM-based chatbot is safe?

Calvin's LLM Assessment features provide standardized, calibrated metrics to validate performance, robustness, and bias of LLM-based chatbots. By leveraging these metrics, organizations can confidently launch chatbots while ensuring both technical quality and fairness.

How does Calvin Risk speed up LLM validation?

Our platform offers automated paraphrasing and pre-trained classifiers to streamline validation processes. This automation increases efficiency by reducing manual efforts and accelerating the assessment of LLM solutions.

Can Calvin Risk support me with the monitoring and updating of my LLM applications?

Calvin's LLM Assessment framework is built to assist organizations with continuous monitoring of LLMs’ performance, implementation of improvements, and incident response plans development.

What types of metrics does Calvin's LLM Assessment cover for performance and robustness?

Calvin's LLM Assessment calculates 6 performance metrics and 11 robustness metrics to comprehensively evaluate the technical quality of LLM solutions. These metrics provide insights into performance levels and robustness under several different conditions.

How does Calvin's LLM Assessment address fairness and explainability in LLM-based chatbots?

Our platform analyzes bias using 2 fairness metrics and enhances explainability through pre-trained classifiers on intermediate results. This approach promotes fairness and transparency in LLM implementations, aligning with ethical AI practices.

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