Uncertainty quantification in LLMs is primarily approached through three methods: logit-based methods (analyzing internal probability distributions), sampling-based methods (assessing variability across multiple generations), and verbalized confidence (prompting the model to express its own confidence).

Authors

• Person: Not available Organization: Zylos
  LLM Hallucination Detection and Mitigation: State of the Art in 2026

Sources

• LLM Hallucination Detection and Mitigation: State of the Art in 2026 zylos.ai Zylos via serper

Referenced by nodes (2)

• Large Language Models concept
• Uncertainty quantification concept