chengtang at SemEval-2026 Task 7: A Retrieval-Augmented Generation Framework for Cultural Perspective Alignment in Everyday MCQs
Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) often exhibit significant cultural representation biases in multilingual everyday knowledge understanding, struggling to accurately capture regionspecific customs and values.This paper presents our system submission for SemEval 2026 Task 7: BLEnD Challenge Track 2 (MCQ) (Ousidhoum et al., 2026).To address these challenges, we propose a training-free retrieval-augmented generation (RAG) framework.Without introducing any external data, we manually constructed a localized multicultural knowledge base for each languageregion and used text-embedding-v4 for regionspecific cultural background retrieval.In the generation stage, we adopted a strict zeroshot setting: prompts contain no task instance question-answer examples, only injecting locale-relevant background cultural descriptions via RAG to compensate for contextual information absence, combined with a dual-model ensemble strategy using Gemini 3 Flash (preview) (Google DeepMind, 2025) and GPT-5.2Chat (OpenAI, 2025).Our system achieved an overall score of 96.35 on the final Evaluation dataset.Additionally, we conducted in-depth analysis of model performance on specific languages, particularly highlighting severe cultural alignment challenges faced by large models in dialectal variants like Moroccan Arabic (ar-MA) and highly localized subjective Japanese (ja-JP) everyday scenarios.
Authors: Cheng Tang, Zhichao Meng, Meizhi Jin
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166889249