Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence

2026-06-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A new methodology is introduced to quantify aleatoric uncertainty (AU) in In-Context Learning (ICL) for Large Language Models (LLMs), addressing the challenge of distinguishing data ambiguity from model limitations. This approach, developed by researchers at POSTECH, leverages "self-function vectors" derived from internal model representations, building on Bayesian views and mechanistic interpretability. The work also proposes the first rigorous evaluation protocol for uncertainty decomposition in ICL, initially using synthetic tasks and then extending to real-world datasets like WordNetMCQ. Experiments with models such as LLaMA2-7B, LLaMA2-13B, LLaMA2-70B, Qwen2.5-7B, and Mistral-7B demonstrate that this method measures LLM prediction uncertainty more reliably than existing alternatives and offers practical utility in applications like hallucination detection.

Key takeaway

For AI Scientists and MLOps Engineers building trustworthy LLM applications, relying solely on total uncertainty metrics for In-Context Learning is insufficient. You should consider adopting mechanistic uncertainty decomposition methods, such as those using self-function vectors, to better diagnose prediction failures and enhance reliability. Implementing the proposed evaluation protocol can also help validate your models' uncertainty estimates, particularly for critical tasks like hallucination detection.

Key insights

Self-function vectors enable robust aleatoric and epistemic uncertainty decomposition in LLM In-Context Learning.

Principles

• Uncertainty decomposition clarifies LLM failure modes.
• Internal model representations can quantify uncertainty.
• Larger LLMs show clearer uncertainty source separation.

Method

Identify causal attention heads, construct self-function vectors from final-token activations, inject them into hidden states, then compute decomposed uncertainties using latent-conditioned predictions.

In practice

• Apply self-function vectors for hallucination detection.
• Use WordNetMCQ for controlled uncertainty evaluation.
• Intervene at one-third transformer depth (e.g., layer 10 for LLaMA2-7B).

Topics

• In-Context Learning
• Uncertainty Quantification
• Aleatoric Uncertainty
• Epistemic Uncertainty
• Mechanistic Interpretability
• Large Language Models
• Hallucination Detection

Code references

• LOG-postech/self-fv-icl

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

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