Artificial Intelligence · 31.07.2026, 07:03 UTC
AfriEconQA: A Benchmark for Quantitative and Temporal Reasoning over World Bank Economic Reports
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.CL ↗ |
| Veröffentlicht | 31.07.2026 UTC |
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arXiv:2601.15297v3 Announce Type: replace Abstract: Reliable question answering over long institutional documents requires more than topical retrieval: a system must localize the exact passage that supports a claim and preserve precise numerical and temporal detail when the same indicator recurs across years, countries, and projection horizons in dense, repetitive prose. Existing question-answering benchmarks rarely test this combination of exact grounding and temporal precision at document scale. We introduce AfriEconQA, a benchmark for document-grounded question answering built from 220 World Bank economic reports on African economies, a corpus whose claims are tied to specific fiscal periods, countries, and projection states. AfriEconQA contains 4,309 evidence-linked QA instances across five reasoning categories: Factoid (1,093), List (879), Multiple Choice (944), Synthesis (710), and Comparison (683), targeting quantitative extraction, set recovery, discrimination, causal integration, and cross-period or cross-country reasoning. Each instance carries sup- porting evidence and source provenance and is constructed through an agentic generation pipeline with evidence-grounding checks and a stratified human-validation subset for gold-label auditing. We evaluate Qwen 3.6 35B, DeepSeek v4-pro, and Gemma 4 12B IT under zero-shot, oracle, and hybrid retrieval-augmented generation (RAG) conditions on the held-out test split (n = 862). Across all three models, retrieval yields substantial gains, yet the best RAG system reaches only 0.545 F1, with residual errors concentrated in …