The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

2026-06-22 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

A new study, "The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models" (2606.22792), addresses the challenge of LLM stochasticity and its impact on predictive credibility. The authors propose a granular uncertainty taxonomy, categorizing LLM uncertainty into input-level, parameter-level, token-level, and decoding-process sources, and classify existing Uncertainty Quantification (UQ) methods into Bayesian, ensemble, consensus-based, and single-pass approaches. They introduce a comprehensive evaluation framework and empirically assessed 21 UQ methods across Qwen3, Llama 3.2, and DeepSeek-V3 LLM families on benchmarks like TriviaQA, GSM8K, and HumanEval. Experimental results indicate that UQ method effectiveness varies by task, consensus-based methods like Deg and EigV consistently perform best, and larger model scales correlate with lower uncertainty, suggesting an empirical scaling law for LLM uncertainty.

Key takeaway

For Machine Learning Engineers deploying LLMs in critical applications, understanding and quantifying model uncertainty is crucial. You should prioritize consensus-based Uncertainty Quantification methods, specifically Deg and EigV, as they consistently outperform other approaches across various tasks. When selecting a UQ strategy, consider your specific task type and generation settings, as method effectiveness is sensitive to these factors. Additionally, recognize that larger LLM scales generally correlate with lower uncertainty estimates.

Key insights

A new taxonomy and evaluation framework reveal that consensus-based methods excel in quantifying LLM uncertainty, which decreases with model scale.

Principles

• UQ method efficacy depends on task and generation settings.
• Consensus-based UQ methods (Deg, EigV) show superior performance.
• LLM uncertainty exhibits an empirical scaling law with model size.

Method

Proposes a granular uncertainty taxonomy (input, parameter, token, decoding process). Categorizes UQ methods (Bayesian, ensemble, consensus, single-pass). Evaluates 21 UQ methods across 3 LLM families and benchmarks.

In practice

• Prioritize consensus-based UQ methods like Deg or EigV.
• Tailor UQ method selection to specific task types.
• Consider model scale when interpreting uncertainty estimates.

Topics

• Large Language Models
• Uncertainty Quantification
• LLM Evaluation
• Consensus-based Methods
• Model Stochasticity
• Empirical Scaling Laws

Code references

• ODYSSEYWT/GUQ

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.