AfriEconQA: A Benchmark Dataset for African Economic Analysis based on World Bank Reports
Edward Ajayi

TL;DR
AfriEconQA introduces a challenging dataset for African economic analysis, emphasizing high-precision reasoning over specialized reports, revealing significant gaps in current LLM capabilities for domain-specific IR tasks.
Contribution
This paper presents the first benchmark dataset focused on African economic analysis, designed to evaluate and improve IR and RAG systems in a domain with limited pretraining data.
Findings
Zero-shot models answer over 90% of queries incorrectly.
State-of-the-art RAG pipelines struggle with high-precision retrieval.
AfriEconQA is a robust benchmark highlighting knowledge gaps in current models.
Abstract
We introduce AfriEconQA, a specialized benchmark dataset for African economic analysis grounded in a comprehensive corpus of 236 World Bank reports. The task of AfriEconQA is to answer complex economic queries that require high-precision numerical reasoning and temporal disambiguation from specialized institutional documents. The dataset consists of 8,937 curated QA instances, rigorously filtered from a pool of 10018 synthetic questions to ensure high-quality evidence-answer alignment. Each instance is composed of: (1) a question requiring reasoning over economic indicators, (2) the corresponding evidence retrieved from the corpus, (3) a verified ground-truth answer, and (4) source metadata (e.g., URL and publication date) to ensure temporal provenance. AfriEconQA is the first benchmark focused specifically on African economic analysis, providing a unique challenge for Information…
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Taxonomy
TopicsComputational and Text Analysis Methods · Topic Modeling · Stock Market Forecasting Methods
