Learning Uncertainty from Sequential Internal Dispersion in Large Language Models

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Researchers from Nanyang Technological University, Shanghai Jiao Tong University, and VinUniversity introduce Sequential Internal Variance Representation (SIVR), a supervised framework for detecting hallucinations in large language models (LLMs). SIVR addresses limitations of prior methods by leveraging token-wise, layer-wise features derived from hidden states, specifically focusing on the dispersion or variance of internal representations across layers. It aggregates the full sequence of per-token variance features, including generalized variance, circular variance, and predictive entropy, to learn temporal patterns indicative of factual errors. Experimental results across twelve datasets and models like Llama-3.2-3B, Llama-3.1-8B, and Ministral-8B show SIVR consistently outperforms strong baselines, achieving average improvements of 3.06% in AUC, 7.53% in FPR@95, and 4.19% in AUPR. Crucially, SIVR demonstrates stronger generalization to out-of-distribution settings and requires smaller training datasets, enhancing its practical deployment potential.

Key takeaway

For research scientists developing or deploying LLMs in high-stakes applications, SIVR offers a robust and generalizable method for hallucination detection. You should consider integrating SIVR's internal variance features and sequence-aware classification into your uncertainty estimation pipelines, especially when cross-task generalization is critical or large labeled datasets are scarce. This approach can significantly improve the reliability of LLM outputs and reduce the risk of factual errors in production.

Key insights

Uncertainty in LLMs can be reliably inferred from the dispersion of hidden states across layers and token sequences.

Principles

• Uncertainty manifests as dispersion in internal representations.
• Full token sequence analysis prevents information loss.
• Internal variance features enhance OOD generalization.

Method

SIVR computes token-wise, layer-wise internal variance (generalized variance, circular variance, token entropy) from LLM hidden states, then uses a transformer encoder to learn sequence-level patterns for hallucination detection.

In practice

• Use internal variance features for robust hallucination detection.
• Aggregate full token sequences to capture temporal error patterns.
• SIVR works effectively with small training datasets (e.g., 128 instances).

Topics

• Hallucination Detection
• Large Language Models
• Uncertainty Estimation
• Sequential Internal Variance Representation
• Internal Variance Features

Code references

• ponhvoan/internal-variance

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

Related on AIssential

• Learning Uncertainty from Sequential Internal Dispersion in Large Language Models — SIVR detects LLM hallucinations by analyzing internal representation variance across layers and tokens.
• From Out-of-Distribution Detection to Hallucination Detection: A Geometric View — Reframing LLM hallucination detection as OOD detection offers a training-free, single-sample, scalable solution for reasoning tasks.
• Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence — Self-function vectors enable robust aleatoric and epistemic uncertainty decomposition in LLM In-Context Learning.
• Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads — RAUQ efficiently detects LLM hallucinations by analyzing specific "uncertainty-aware" attention head patterns in a single forward pass.
• AI is Confidently Lying to You. MIT Just Figured Out How to Catch It — MIT's Total Uncertainty metric detects AI hallucinations by combining internal confidence with cross-model disagreement.
• Weakly Supervised Distillation of Hallucination Signals into Transformer Representations — Hallucination signals can be distilled into LLM internal representations for detection without external inference-time resources.

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