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Groundedness detection detects whether the text responses of large language models are grounded in the source materials provided by the users. Ungroundedness refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials.
In this demo, we’ll demonstrate the model’s ability to detect grounded and ungrounded output for both a Q&A conversation structure and summarization.
Disclosure: This demo contains an AI-generated voice.
Chapters:
00:00 – Introduction
00:28 – Q&A task
01:27 – Summarization task
Resources:
Azure AI Studio – https://ai.azure.com
Learn Module – https://aka.ms/aacs-studio-workshop Read More Microsoft Developer
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