• Seattle Skeptics on AI
Seattle Skeptics on AI

Tag: AI detection tools

How to Detect Fabricated References in LLM Outputs: A Guide for Researchers
How to Detect Fabricated References in LLM Outputs: A Guide for Researchers

Tamara Weed, Jul, 18 2026

Discover how to detect fabricated references in LLM outputs. Learn why AI creates ghost citations, the risks to academic integrity, and tools like CERCA to verify sources.

Categories:

Science & Research

Tags:

fabricated references LLM hallucinations citation verification academic integrity AI detection tools

Recent post

  • Transformer Architecture Explained: A Technical Deep Dive into LLMs
  • Transformer Architecture Explained: A Technical Deep Dive into LLMs
  • Prompt Robustness: A Practical Guide to Handling Noisy Inputs in LLM Systems
  • Prompt Robustness: A Practical Guide to Handling Noisy Inputs in LLM Systems
  • Human Review Workflows: Ensuring Accuracy in High-Stakes AI Responses
  • Human Review Workflows: Ensuring Accuracy in High-Stakes AI Responses
  • Domain-Specialized Generative AI Models: Why Industry-Specific AI Outperforms General Models
  • Domain-Specialized Generative AI Models: Why Industry-Specific AI Outperforms General Models
  • Trustworthy AI for Code: How Verification, Provenance, and Watermarking Are Changing Software Development
  • Trustworthy AI for Code: How Verification, Provenance, and Watermarking Are Changing Software Development

Categories

  • Enterprise Technology
  • Science & Research

Archives

  • October 2026
  • September 2026
  • August 2026
  • July 2026
  • June 2026
  • May 2026
  • April 2026
  • March 2026
  • February 2026
  • January 2026
  • December 2025
  • November 2025

Tags

vibe coding prompt engineering generative AI large language models transformer architecture LLM security AI governance Large Language Models prompt injection data privacy AI coding tools AI development responsible AI AI compliance AI code generation multimodal generative AI LLM optimization transformer models enterprise AI LLM architecture

© 2026. All rights reserved.