This podcast is based on sources that collectively explore the challenges and considerations surrounding the use of generative artificial intelligence (AI), particularly in natural language generation (NLG). They discuss problems and pitfalls associated with AI-generated text, including the production of misinformation and bias, as well as the potential for plagiarism and lack of originality. The articles highlight the phenomenon of AI hallucinations, where models generate fabricated information or sources, and introduce AttributionBench as a benchmark to evaluate the accuracy of AI in attributing claims to evidence. Furthermore, one source provides a guide on using NotebookLM, an AI-powered tool, while acknowledging its limitations, such as its inability to fully scan website content. Overall, the texts emphasize the importance of critically evaluating AI-generated content and using AI tools responsibly to avoid issues and ensure accuracy and integrity.
Source:
2.6: Problems and Pitfalls of AI-Generated Texts - Humanities LibreTextsdrive_pdfAttributionBench: How Hard is Automatic Attribution Evaluation? - ACL AnthologywebHow NotebookLM prevents the 3 biggest factual disasters in AI content - SubstackwebHow To Fact-Check AI Content Like a Pro - ArticulatewebHow to Use NotebookLM: Step by Step Guide - The Wursta CorporationwebNotebookLM as a research tool - asking for outside knowledge? (outside the sources)webNotebookLM: How to try Google's experimental AI-first notebookdrive_pdfSurvey of Hallucination in Natural Language Generation - arXivwebUsing Generative AI: Evaluating AI Content - University of Alberta Library Subject Guides
