迈向文化扎根的自然语言处理
Toward Culturally Grounded Natural Language Processing
AI总结：
本文探讨多语言NLP中文化素养与语言能力的脱节问题，提出通过丰富上下文元数据和文化分层评估等方法，推动文化扎根的NLP研究。
摘要：
近年来多语言NLP的进展常被视为全球包容性的证据，但越来越多的文献表明多语言能力与文化素养存在脱节。本文综合了2020-2026年间50多篇论文，涵盖多语言表现不平等、跨语言迁移、文化感知评估、文化对齐、多模态本地知识建模、基准设计批评和社区扎根数据实践。在该文献中，训练数据覆盖率仍是性能的关键决定因素，但并不充分：分词、提示语言、翻译的基准设计、文化特定的监督以及多模态上下文都显著影响结果。最近关于Global-MMLU、CDEval、WorldValuesBench、CulturalBench、CULEMO、CulturalVQA、GIMMICK、DRISHTIKON、WorldCuisines、CARE、CLCA以及新基准设计和社区扎根评估的批评表明，即使强大的多语言模型仍可能扁平化本地规范、误读文化扎根的提示，并在低资源或社区特定设置中表现不佳。本文认为，该领域应从将语言视为基准表格中的孤立行转向建模交际生态：机构、脚本、翻译流程、领域、模态和社区等通过语言使用的渠道。基于此，我们提出以更丰富的上下文元数据、文化分层评估、参与性对齐、语言内变异和多模态社区感知设计为中心的文化扎根NLP研究议程。
英文摘要：
Recent progress in multilingual NLP is often taken as evidence of broader global inclusivity, but a growing literature shows that multilingual capability and cultural competence come apart. This paper synthesizes over 50 papers from 2020--2026 spanning multilingual performance inequality, cross-lingual transfer, culture-aware evaluation, cultural alignment, multimodal local-knowledge modeling, benchmark design critiques, and community-grounded data practices. Across this literature, training data coverage remains a strong determinant of performance, yet it is not sufficient: tokenization, prompt language, translated benchmark design, culturally specific supervision, and multimodal context all materially affect outcomes. Recent work on Global-MMLU, CDEval, WorldValuesBench, CulturalBench, CULEMO, CulturalVQA, GIMMICK, DRISHTIKON, WorldCuisines, CARE, CLCA, and newer critiques of benchmark design and community-grounded evaluation shows that strong multilingual models can still flatten local norms, misread culturally grounded cues, and underperform in lower-resource or community-specific settings. We argue that the field should move from treating languages as isolated rows in a benchmark spreadsheet toward modeling communicative ecologies: the institutions, scripts, translation pipelines, domains, modalities, and communities through which language is used. On that basis, we propose a research agenda for culturally grounded NLP centered on richer contextual metadata, culturally stratified evaluation, participatory alignment, within-language variation, and multimodal community-aware design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
