Capturing Epistemic Uncertainty in LLM-Based Soft Labeling

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new approach formalizes LLM-based soft labeling to capture epistemic uncertainty in human-annotated NLP tasks, where annotator disagreement reflects genuine ambiguity. This method introduces controlled variation in model-generated annotations to approximate latent human variability. Researchers distinguish two variation sources: model-induced (e.g., stochastic decoding, model ensembles) and human-approximated (e.g., persona prompting, human-calibrated in-context annotation). Using the Gab Hate and GoEmotions datasets, soft labeling (SL) training consistently outperformed majority-vote (MV) training under stronger LLM annotation strategies. Model ensembles produced the most informative soft-label distributions, achieving superior human–LLM agreement and downstream classification performance. This suggests scalable LLM annotation pipelines can model epistemic uncertainty through diverse model-level variation without explicitly simulating human attributes.

Key takeaway

For NLP Engineers building scalable annotation pipelines, you should integrate LLM-based soft labeling to capture inherent epistemic uncertainty in ambiguous tasks. By leveraging model ensembles, your pipelines can generate more informative label distributions, leading to improved human-LLM agreement and better downstream classification performance compared to traditional majority-vote methods. Consider experimenting with stochastic decoding and persona prompting to introduce controlled variation in your LLM annotations.

Key insights

LLMs can capture epistemic uncertainty in soft labeling via controlled variation, improving downstream NLP task performance.

Principles

• Annotator disagreement can signal epistemic uncertainty, not just noise.
• Soft labeling preserves uncertainty better than majority-vote labels.
• Model ensembles yield highly informative soft-label distributions.

Method

LLM soft labeling introduces controlled variation in model-generated annotations to approximate human variability, using model-induced (stochastic decoding, ensembles) and human-approximated (persona prompting, in-context annotation) sources.

In practice

• Employ LLM-based soft labeling for ambiguous NLP annotation tasks.
• Utilize model ensembles to generate robust soft-label distributions.
• Prioritize soft labeling (SL) training over majority-vote (MV) for better performance.

Topics

• LLM-based Annotation
• Soft Labeling
• Epistemic Uncertainty
• Model Ensembles
• NLP Tasks
• Stochastic Decoding
• Persona Prompting

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.