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Detect and Mitigate Ungrounded Model Outputs

kenarcher's avatar
kenarcher
Icon for Microsoft rankMicrosoft
Mar 28, 2024

Today, we are pleased to announce “Groundedness Detection,” alongside other advancements and feature announcements available in Azure AI. 

 

Ungrounded model outputs are consistently cited as a top risk to broad deployments of Copilots and other Generative AI-powered applications, particularly in high-stakes domains such as law, medicine and finance.   

 

Ungroundedness, otherwise known as hallucination, refers to model outputs that are plausible yet unsupported.  Whereas research shows that ungrounded output is an inherent feature of Generative AI models, it can be significantly reduced through continuous monitoring and mitigation.  But this requires a way to detect ungrounded outputs at a greater scale than is possible with manual checks.  Today Azure AI makes this possible for the first time with groundedness detection.    

  

This feature detects ungrounded statements within Generative AI output in applications using grounded documents, such as Q&A Copilots and document summarization applications.  When an ungrounded claim is detected, customers can take one of numerous mitigation steps: 

  • Test their AI implementation pre-deployment against groundedness metrics,   
  • Highlight ungrounded statements for internal users, triggering fact checks or mitigations such as metaprompt improvements or knowledge base editing,  
  • Trigger a rewrite of ungrounded statements before returning the completion to the end user, or   
  • When generating synthetic data, evaluate the groundedness of synthetic training data before using it to fine-tune their language model.  

How does Groundedness Detection work?  

Previously, some Generative AI applications would chain a request to an LLM asking if a completion was grounded relative to a grounding document.  This ad hoc approach has resulted in insufficient recall of ungrounded claims to derisk Generative AI applications.   

Azure AI’s groundedness detection feature is built from the ground up to accurately detect ungrounded claims.  We built a custom language model fine-tuned to a natural language processing task called Natural Language Inference (NLI), which evaluates claims as being entailed, refuted by, or neutral with regard to a source document.    

 Azure AI Content Safety’s groundedness detection model will continuously improve as Microsoft continues to push the envelope of Responsible AI innovation. 

 

 

Resources: 

Updated Mar 28, 2024
Version 1.0

2 Comments

  • Quest198z's avatar
    Quest198z
    Copper Contributor

    Are there ways or recommendations to test this out in Azure DevOps or Github pipelines as people start deploying applications that are tightly coupled with Azure Open AI and Azure AI studio?  Are there approved scanners or AzDo/Github Tasks we can incorporate?  As we start deploying products with these features, we would need a way to test them prior to promoting to production.

  • Read our newest Azure blog to learn more about all of our responsible AI features announced today:

    • Prompt Shields to detect and block prompt injection attacks, including a new model for identifying indirect prompt attacks before they impact your model, coming soon and now available in preview in Azure AI Content Safety. 
    • Safety evaluations to assess an application’s vulnerability to jailbreak attacks and to generating content risks, now available in preview.  
    • Risk and safety monitoring to understand what model inputs, outputs, and end users are triggering content filters to inform mitigations, coming soon, and now available in preview in Azure OpenAI Service.
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and Mitigate Ungrounded Model Outputs","id":"message:4099261","revisionNum":4,"repliesCount":2,"author":{"__ref":"User:user:2386135"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"conversation":{"__ref":"Conversation:conversation:4099261"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":null},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:4099261"},"teaser":"

Azure is the first cloud platform to enable you to detect and mitigate ungrounded, sometimes called hallucinated, model outputs. 

\n

\n

 

","body":"

Today, we are pleased to announce “Groundedness Detection,” alongside other advancements and feature announcements available in Azure AI. 

\n

 

\n

Ungrounded model outputs are consistently cited as a top risk to broad deployments of Copilots and other Generative AI-powered applications, particularly in high-stakes domains such as law, medicine and finance.   

\n

 

\n

Ungroundedness, otherwise known as hallucination, refers to model outputs that are plausible yet unsupported.  Whereas research shows that ungrounded output is an inherent feature of Generative AI models, it can be significantly reduced through continuous monitoring and mitigation.  But this requires a way to detect ungrounded outputs at a greater scale than is possible with manual checks.  Today Azure AI makes this possible for the first time with groundedness detection.    

\n

  

\n

This feature detects ungrounded statements within Generative AI output in applications using grounded documents, such as Q&A Copilots and document summarization applications.  When an ungrounded claim is detected, customers can take one of numerous mitigation steps: 

\n\n

How does Groundedness Detection work?  

\n

Previously, some Generative AI applications would chain a request to an LLM asking if a completion was grounded relative to a grounding document.  This ad hoc approach has resulted in insufficient recall of ungrounded claims to derisk Generative AI applications.   

\n

Azure AI’s groundedness detection feature is built from the ground up to accurately detect ungrounded claims.  We built a custom language model fine-tuned to a natural language processing task called Natural Language Inference (NLI), which evaluates claims as being entailed, refuted by, or neutral with regard to a source document.    

\n

 Azure AI Content Safety’s groundedness detection model will continuously improve as Microsoft continues to push the envelope of Responsible AI innovation. 

\n

 

\n

 

\n

Resources: 

\n","body@stringLength":"9696","rawBody":"

Today, we are pleased to announce “Groundedness Detection,” alongside other advancements and feature announcements available in Azure AI. 

\n

 

\n

Ungrounded model outputs are consistently cited as a top risk to broad deployments of Copilots and other Generative AI-powered applications, particularly in high-stakes domains such as law, medicine and finance.   

\n

 

\n

Ungroundedness, otherwise known as hallucination, refers to model outputs that are plausible yet unsupported.  Whereas research shows that ungrounded output is an inherent feature of Generative AI models, it can be significantly reduced through continuous monitoring and mitigation.  But this requires a way to detect ungrounded outputs at a greater scale than is possible with manual checks.  Today Azure AI makes this possible for the first time with groundedness detection.    

\n

  

\n

This feature detects ungrounded statements within Generative AI output in applications using grounded documents, such as Q&A Copilots and document summarization applications.  When an ungrounded claim is detected, customers can take one of numerous mitigation steps: 

\n\n

How does Groundedness Detection work?  

\n

Previously, some Generative AI applications would chain a request to an LLM asking if a completion was grounded relative to a grounding document.  This ad hoc approach has resulted in insufficient recall of ungrounded claims to derisk Generative AI applications.   

\n

Azure AI’s groundedness detection feature is built from the ground up to accurately detect ungrounded claims.  We built a custom language model fine-tuned to a natural language processing task called Natural Language Inference (NLI), which evaluates claims as being entailed, refuted by, or neutral with regard to a source document.    

\n

 Azure AI Content Safety’s groundedness detection model will continuously improve as Microsoft continues to push the envelope of Responsible AI innovation. 

\n

 

\n

 

\n

Resources: 

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Azure is the first cloud platform to enable you to detect and mitigate ungrounded, sometimes called hallucinated, model outputs. 

\n

\n

 

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Are there ways or recommendations to test this out in Azure DevOps or Github pipelines as people start deploying applications that are tightly coupled with Azure Open AI and Azure AI studio?  Are there approved scanners or AzDo/Github Tasks we can incorporate?  As we start deploying products with these features, we would need a way to test them prior to promoting to production.

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Read our newest Azure blog to learn more about all of our responsible AI features announced today:

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