Wrong Output evaluation with default questions - LLMs Evaluation with Azure AI Studio

ferrand 50 Reputation points
2024-02-27T21:47:34.63+00:00

Dear Microsoft community, I am following step by step this official tutorial: https://learn.microsoft.com/en-us/azure/ai-studio/tutorials/deploy-copilot-ai-studio#customize-prompt-flow-with-multiple-data-sources When I run the evaluation I get a result with the questions and answers I prepared on the test dataset. This is right:
User's image

However, the result with evaluation metrics is wrong. The questions of the test dataset are not considered. Instead I get always the same default question and answer pair: User's image

How can I solve this problem? I used the custom evaluation instead of the built-in evaluation. When I use the built-in evaluation I get this other issue that I posted here https://learn.microsoft.com/en-us/answers/questions/1598940/error-flow-runtime-not-found-llms-built-in-evaluat.

Thanks in advance for your kind support.

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
2,838 questions
Azure AI Search
Azure AI Search
An Azure search service with built-in artificial intelligence capabilities that enrich information to help identify and explore relevant content at scale.
943 questions
Azure OpenAI Service
Azure OpenAI Service
An Azure service that provides access to OpenAI’s GPT-3 models with enterprise capabilities.
2,899 questions
Azure AI services
Azure AI services
A group of Azure services, SDKs, and APIs designed to make apps more intelligent, engaging, and discoverable.
2,775 questions
{count} votes

1 answer

Sort by: Most helpful
  1. ferrand 50 Reputation points
    2024-02-28T21:54:41.37+00:00

    @romungi-MSFT I changed the model to GPT4-32k and used a jsonl format instead of csv. I made it. Thanks a lot for your support.

    1 person found this answer helpful.
    0 comments No comments

Your answer

Answers can be marked as Accepted Answers by the question author, which helps users to know the answer solved the author's problem.