Uncertainty, Confidence, and Hallucination in Large Language Models

Table of Content

• LLM Is Just Making Stuff Up

• Detecting Deception: Tools and Methods for Identifying LLM Falsehoods

• Score-based Approaches for Uncertainty Estimation in LLMs

  • Heuristic Uncertainty as a Clue

  • Quantifying Uncertainty with Information Theory

• Model-based Hallucination Detection

  • LLM as Evaluators

  • Simple Conformal Predictors

• Final Thoughts: The Future of LLM Hallucination Detection

LLM Is Just Making Stuff Up

Ever have a conversation with a large language model that sounds super confident, spitting out facts that seem…well, a little fishy? 🐟 You’re not alone. One of the biggest challenges in working with Large Language Models (LLMs) is verifying the correctness of their output. Despite their advanced capabilities, LLMs can sometimes generate information that appears accurate but is fabricated. This phenomenon, known as 👉 hallucination, can lead to misinformation and erode trust in AI systems. Hallucination in AI is not a new phenomenon. Deep learning models, in general, are notorious for their over-confidence in predictions. For instance, in classification tasks, these models can assign a very high probability to a label prediction, even when the prediction is incorrect [1]. Deep learning models can be misleading in how powerful they truly are.

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