Learn Hands-on Skills to Minimize Hallucinations in AI-Generated Content

Content summary:
Learn Hands-on Skills to Minimize Hallucinations in AI-Generated Content
Given the rapid integration of AI writing tools into our academic and research environments, the need to ensure the accuracy and reliability of AI-generated content has never been more pressing. We invite faculty and students from WashU to join us in a comprehensive workshop designed to address and reduce hallucinations in large language models.
Why Attend?
– Addressing Key Pain Points: Are you concerned about non-existent citations, non-factual mistakes, or errors in interpretation? This workshop targets these issues head-on, providing you with the skills to identify and correct these errors in AI-generated content.
– Objective Insights: Receive a balanced overview of proven strategies to minimize hallucinations in AI outputs. Our focus is on delivering practical, evidence-based methods that enhance the trustworthiness of AI-generated content.
– Concise and Impactful Learning: Through concise case studies and examples, gain hands-on experience in prompt engineering, custom tooling, fact-checking, and database grounding. Learn to navigate and correct common AI-generated errors efficiently.
This workshop is crafted to meet the needs of both faculty and students, providing essential skills for anyone looking to improve the reliability of AI-generated content. Whether you’re seeking to understand the nuances of AI-generated errors or looking for effective ways to ensure the accuracy of your AI tools, this workshop will offer valuable insights and practical solutions.

Presenter: Ruopeng An

Slides used in this video can be downloaded from GitHub:

Hashtags: #artificialintelligence #machinelearning #deeplearning #python #pythonprogramming #pythontutorial #aitutorial #coding #neuralnetworks #neuralnetwork #pytorch #computervision #nlp #naturallanguageprocessing #scikitlearn