Uncertainty Quantification for Large Language Diffusion Models

2026-05-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Natural Language Processing · Depth: Expert, extended

Summary

A systematic study introduces novel uncertainty quantification (UQ) methods for Large Language Diffusion Models (LLDMs), which are emerging as efficient alternatives to autoregressive LLMs but remain prone to hallucinations. Existing UQ methods are misaligned with LLDMs, either assuming autoregressive factorization or requiring expensive repeated sampling that negates LLDMs' efficiency. This work proposes lightweight, zero-shot uncertainty signals derived from the iterative denoising process, including intermediate generations, token remasking dynamics, and denoising complexity. It also adapts the state-of-the-art UQ method CoCoA to LLDMs, creating D-CoCoA, by combining masked diffusion likelihoods with trajectory-based semantic dissimilarity. Experiments across three tasks, eight datasets, and two models (LLaDA-1.5 and Dream) demonstrate that D-CoCoA achieves a strong cost-performance trade-off, approaching sampling-based baselines with up to 100x lower computational overhead, proving LLDMs can offer both fast inference and reliable hallucination detection.

Key takeaway

For research scientists developing or deploying Large Language Diffusion Models, you should integrate diffusion-specific uncertainty quantification methods like D-CoCoA. This approach allows for reliable hallucination detection without sacrificing the inherent efficiency of LLDMs, offering a superior performance-efficiency trade-off compared to traditional autoregressive UQ methods. Consider using D-CoCoA-G for comprehensive global uncertainty or D-CoCoA-L for localized confidence assessments in your applications.

Key insights

Diffusion-specific signals enable efficient and effective uncertainty quantification in Large Language Diffusion Models.

Principles

• Intermediate denoising states offer rich UQ signals.
• Trajectory dissimilarity lower bounds LLDM masking loss.
• Efficiency is paramount for LLDM UQ methods.

Method

The D-CoCoA method adapts CoCoA for LLDMs by replacing its log-likelihood with a diffusion-aware surrogate and its sampling component with intermediate denoising trajectory states, using signals like masked diffusion likelihoods and trajectory semantic dissimilarity.

In practice

• Use D-CoCoA-G for global semantic drift detection.
• Employ D-CoCoA-L for local token-level confidence.
• Prioritize progress-weighted trajectory dissimilarity.

Topics

• Large Language Diffusion Models
• Uncertainty Quantification
• Hallucination Detection
• Denoising Trajectory Signals
• D-CoCoA Method

Best for: Research Scientist, AI Scientist, NLP Engineer, MLOps Engineer

Related on AIssential

• Uncertainty Quantification for Large Language Diffusion Models — Lightweight, zero-shot uncertainty quantification for LLDMs can achieve high performance with significantly reduced computational cost.
• Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation — Re-evaluating Uniform Diffusion Model parameterization and sampling design is crucial for improving discrete diffusion model performance.
• DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs — Adapting pretrained masked diffusion LMs with continuous denoising via soft masking resolves few-step generation tradeoffs.
• One-step Language Modeling via Continuous Denoising — Continuous denoising via flow-based models can surpass discrete diffusion in language model quality and speed.
• Mind the Unseen Mass: Unmasking LLM Hallucinations via Soft-Hybrid Alphabet Estimation — SHADE improves LLM uncertainty quantification by adaptively fusing missing-mass extrapolation and spectral graph analysis, especially with few samples.
• Learning Uncertainty from Sequential Internal Dispersion in Large Language Models — Uncertainty in LLMs can be reliably inferred from the dispersion of hidden states across layers and token sequences.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.