Introduction
This article aims to identify “hallucinations” in large language model (LLM) outputs and provide insights into the features of groundedness detection. Additionally, it covers use cases for groundedness detection, as well as its region and language support.
Groundedness Detection API
The Groundedness detection API assesses whether the text generated by large language models (LLMs) is well-supported by the input provided by users. Ungroundedness occurs when LLMs produce information that deviates from the factual content present in the source materials.
Features of Groundedness detection
- Domain Specification: Users have the option to select a well-defined domain, which ensures more customized detection aligned with the specific requirements of their field. Currently, the available domains are Medical and Generic.
- Task Specification: This functionality allows users to choose their specific task, such as QnA (question and answering) or summarization, and adjust the settings accordingly based on the task type.”
- Speed vs Interpretability: There exist two modes that balance speed and interpretability of results.
- Non-Reasoning mode: Provides rapid detection capabilities and seamless integration into online applications.
- Reasoning mode: Provides comprehensive explanations for identified ungrounded segments, enhancing both understanding and mitigation.
Use Cases
Groundedness detection assists with text-based summarization and QnA tasks, ensuring that the resulting summaries or answers are both accurate and reliable.
Some of the examples are.
Summarization
- Medical Summarization: In the realm of medical news articles, Groundedness detection serves as a valuable tool to verify that summaries remain free from fabricated or misleading content. By doing so, it ensures that readers receive precise and dependable medical information
- Academic paper summarization: In the context of academic paper summarization, this function assists in ensuring that the generated summaries faithfully capture the essential findings and contributions from research articles, all while avoiding any inclusion of false claims
QnA
- Customer Support Chatbots: In the realm of customer support, this function serves to validate the responses generated by AI chatbots. By doing so, it ensures that customers receive precise and reliable information when they inquire about products or services.
- Medical Question and Answer (QnA): When it comes to medical QnA, this function plays a crucial role in verifying the accuracy of medical answers and advice provided by AI systems to both healthcare professionals and patients. Its use helps mitigate the risk of medical errors.
- Educational Question and Answer (QnA): Within educational contexts, this function can be applied to QnA tasks, confirming that answers to academic questions or test preparation queries remain factually accurate. This support contributes to an effective learning process.
Language Support
- The Groundedness Detection API supports English language content only as of now.
Region Support
As of now, it's available in the following Azure regions,
- East US 2
- East US
- West US
- Sweden Central
Summary
In this article, we have gained insights into the purpose, features, and real-time use cases of Groundedness detection. Specifically, we explored its applications in summarization and QnA tasks within both medical and generic domains. Additionally, we delved into the language and regional support provided by this technology.
Happy Learning!
Hope you have enjoyed reading this article!
Join the conversation! Your thoughts help the community grow.