Trustworthy NLP for Low-Resource Languages: Agent-Based Uncertainty Modeling for Hebrew Radiology Report Structuring

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

Summary

A study introduces an agent-based, uncertainty-aware framework designed to enhance the reliability and interpretability of Large Language Model (LLM) predictions in clinical contexts, specifically for structuring complex Hebrew radiology reports. Analyzing 9,683 Hebrew radiology reports from Crohn's disease patients (2010-2023), the framework utilized Llama 3.1 (Llama 3-8b-instruct) with Bayesian Prompt Ensembles (BayesPE) and six semantically equivalent prompts to quantify uncertainty. An Agent-Based Decision Model aggregated these outputs into five calibrated confidence levels. The model achieved an F1 score of 0.3967, recall of 0.6437, and Kappa of 0.3006, outperforming entropy-based baselines. Excluding cases with uncertainty = 0.5 further improved F1 to 0.4787 and Kappa to 0.4258, demonstrating enhanced uncertainty calibration and predictive reliability for safer LLM deployment in medical data extraction.

Key takeaway

For NLP Engineers developing LLM solutions for clinical data extraction, especially with low-resource languages like Hebrew, you should consider implementing agent-based uncertainty modeling. This approach, demonstrated with Llama 3.1 and Bayesian Prompt Ensembles, significantly improves predictive reliability and interpretability. Filtering high-uncertainty outputs can boost your models' F1 scores and Kappa values, enabling safer, more trustworthy deployment in sensitive medical applications.

Key insights

An agent-based framework improves LLM reliability and interpretability for structured data extraction from low-resource, complex medical texts.

Principles

• Quantify LLM uncertainty using prompt ensembles.
• Aggregate confidence levels with decision models.
• Filtering high-uncertainty cases improves performance.

Method

Structured data extraction from Hebrew radiology reports using Llama 3.1 with Bayesian Prompt Ensembles (BayesPE) and an Agent-Based Decision Model to aggregate six prompt outputs into five calibrated confidence levels.

In practice

• Apply BayesPE with multiple prompts for uncertainty.
• Implement agent-based models for confidence calibration.
• Filter LLM outputs based on uncertainty thresholds.

Topics

• Trustworthy NLP
• Low-Resource Languages
• Hebrew Radiology Reports
• Large Language Models
• Uncertainty Quantification
• Agent-Based Models

Best for: AI Scientist, NLP Engineer, Research Scientist

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