From Knowledge to Inference: Scaling Laws of Specialized Reasoning on GlobalHealthAtlas

2026-01-31

AI Summary

This work addresses the lack of structured machine learning frameworks and evaluation benchmarks for public health reasoning, which hinders support for multi-level needs ranging from health literacy to policy-making. It presents GlobalHealthAtlas, the first large-scale, hierarchical, multilingual, and evidence-anchored dataset encompassing 15 domains, 17 languages, and three difficulty levels (280,210 high-quality instances). The authors propose a scalable construction paradigm integrating retrieval-augmented generation, evidence verification, and label validation, alongside a domain-aligned evaluation framework grounded in multidimensional judgment criteria. This benchmark substantially outperforms existing question-answering datasets and effectively supports the training and evaluation of large language models in safety-critical public health reasoning tasks.

Technology Category

• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Public health reasoning requires population level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it remains underexplored as a structured machine learning problem with limited supervised signals and benchmarks. We introduce \textbf{GlobalHealthAtlas}, a large scale multilingual dataset of 280,210 instances spanning 15 public health domains and 17 languages, stratified into three difficulty levels from health literacy to epidemiological and policy reasoning. Instances are derived from openly available public health sources and labeled by language, domain, and difficulty to support supervised learning and slice based evaluation. We further propose large language model (LLM) assisted construction and quality control pipeline with retrieval, duplication, evidence grounding checks, and label validation to improve consistency at scale. Finally, we present a domain aligned evaluator distilled from high confidence judgments of diverse LLMs to assess outputs along six dimensions: Accuracy, Reasoning, Completeness, Consensus Alignment, Terminology Norms, and Insightfulness. Together, these contributions enable reproducible training and evaluation of LLMs for safety critical public health reasoning beyond conventional QA benchmarks.

Problem

• public health reasoning
• supervised signals
• evaluation benchmarks
• machine learning
• safety-critical inference

Innovation

• GlobalHealthAtlas
• specialized reasoning
• LLM-assisted data curation
• domain-aligned evaluation
• public health reasoning