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Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries

Offered By: Neuro Symbolic via YouTube

Tags

Language Models Courses Artificial Intelligence Courses Machine Learning Courses Prompt Engineering Courses Uncertainty Quantification Courses Neuro-Symbolic AI Courses

Course Description

Overview

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Explore a novel approach to detecting errors in language model outputs through "diversity measures" in this 12-minute video. Learn about domain-independent proxies for failure in language model queries, addressing challenges like hallucination. Discover how these measures can be used to assess uncertainty in language model results. Gain insights into the key ideas, self-consistency, and results of this research. Access the preprint and source code for further study. Delve into the exciting intersection of symbolic methods and deep learning, with content derived from an AI course at Arizona State University.

Syllabus

Intro
Key Idea
Selfconsistency
Results


Taught by

Neuro Symbolic

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