wangkongqiang at SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, medium

Summary

The wangkongqiang team developed a system for SemEval-2026 Task 7, focusing on "Everyday Knowledge Across Diverse Languages and Cultures." Their approach evaluated the cultural competence of large language models (LLMs) across 26 languages and 30 countries. They utilized four LLM versions: deepseek-v3.2-exp, qwen-max, qwen-plus, and qwen3-next-80ba3b-instruct. The methodology involved visually analyzing datasets, employing generative LLMs for answer generation or selection via prompts, and extensively evaluating prompt engineering and hyperparameter tuning on a trial dataset. For Subtask 1 (Short Answer Questions), their system achieved an accuracy score of 51.4689, while for Subtask 2 (Multiple-Choice Questions), it reached 80.26 accuracy. The submission secured a good ranking on the test dataset leaderboard.

Key takeaway

For NLP engineers evaluating LLM performance in multilingual and multicultural contexts, this work highlights the critical role of prompt engineering and hyperparameter tuning. Your team should systematically test LLMs like deepseek-v3.2-exp or qwen-max against culturally diverse datasets, similar to SemEval-2026 Task 7, to accurately gauge their "everyday knowledge" and refine deployment strategies for global applications.

Key insights

LLMs demonstrate varying cultural competence across 26 languages and 30 countries, improvable via prompt engineering.

Principles

• Prompt engineering significantly impacts LLM performance.
• Hyperparameter tuning refines generative model outputs.
• Cultural competence varies across diverse linguistic contexts.

Method

The system visually analyzes datasets, uses generative LLMs for answer generation/selection via prompts, evaluates prompt engineering, and tunes hyperparameters to select the best model.

In practice

• Test LLMs on diverse cultural datasets.
• Iterate prompt designs for specific tasks.
• Optimize hyperparameters for target LLM performance.

Topics

• SemEval-2026 Task 7
• Large Language Models
• Cultural Competence
• Multilingual NLP
• Prompt Engineering
• Hyperparameter Tuning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.