Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Although Large Language Models (LLMs) demonstrate excellent capabilities and performance for general reasoning tasks within the general public domain, they may face challenges with culturally grounded knowledge within languages with limited digital and textual data. In this paper, we investigate culturally grounded multiple-choice question answering with the BLEnD benchmark, which consists of a multilingual corpus of 30 languages and covers various socio-cultural domains, such as cuisine, sports, family, etc. We propose a region-aware hybrid retrieval approach that combines BM25 lexical matching and dense semantic similarity with regional weighting heuristics to improve the relevance of the answer. The retrieved documents are used to construct a structured prompt for the Qwen3-14B quantized model with logit-based deterministic answer selection. The experimental results show improvements to cross-lingual stability with the hybrid retrieval approach over pure parametric inference for culturally grounded question answering. However, there are still notable performance gaps between languages with more and less training data. This shows that the limitations of the retrieval augmentation approach are not entirely overcome by the training data imbalance problem.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 18 references that are in the map. First posted on arXiv 26 May 2026.
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6 of 18 references.
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- 2025Qwen3 Technical Report14 May
Other references · 5
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
- 2024BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languagescited by 173
- 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
- 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 27
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- 2025CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis
- 2020Pivot Through English: Reliably Answering Multilingual Questions without Document Retrieval
- 2026No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding
- 2026Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling
- 2026Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks
- 2026MSQA: A Natively Sourced Multilingual and Multicultural SimpleQA Benchmark
- 2026Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
- 2025CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering