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Calvin Risk Platform
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LLM Assessment
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LLM Fairness and Explainability

Detect bias and hallucinations

Model answers must be respectful to different demographic groups, use positive language, and reflect the retrieved contents.  

Test setup

Provide a set of test questions, reference retrieved contents, and your RAG endpoint. Calvin will collect the answers and assess their fairness and explainability.

Fairness

Calculate the amount of bias or toxic language in your system's answers, using calibrated and standardised text classifiers from Calvin.

Explainability

Calculate the semantic distance between answers and reference retrieved contents. If the system generates answers that are not close to the retrieved contents, this may point to hallucinations.

Related Features

LLM Performance and Robustness

Assure correctness and reliability as part of your launch plan.

Learn more

LLM Safety and Accountability

Promote responsible AI and avoid harm.

Learn more

Additional Resources

Blog: Decoding LLM Risks

Decoding LLM Risks: A Comprehensive Look at Unauthorized Code Execution

Infographic: Visual Guide to LLM Concepts

Visual Guide to LLM Concepts

Infographic: Best Practices for LLM Chatbots

Best Practices for LLM Chatbots

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