从文化遗产保护视角重新思考印度人工智能
Rethinking Indic AI from a Lens of Cultural Heritage Preservation
AI总结：
本文从文化遗产保护角度探讨人工智能对印度语言文化的影响，通过纵向调查自然语言处理技术演变、分析印度语言特征及基础模型作用，提出‘文化感知’方向，为印度自然语言处理下一阶段研究及基础模型发展提供指引。
摘要：
随着人工智能进入印度次大陆不同地区，研究其对该文明语言和文化基础的影响备受关注。人工智能是把双刃剑，一方面能让大量人群受益，另一方面会使世界观同质化并排斥未被充分代表的语言和世界观。本文通过探讨印度语言学的广泛特性及其与文化实践和世界观的紧密联系来刻画此问题。对自然语言处理技术在该领域的演变进行纵向调查，追溯印度自然语言处理的历史发展。研究印度语言的结构和社会语言学特征给构建人工智能基础模型带来的独特挑战。讨论印度基础模型的作用并分析其如何解决资源和表示差距。最后提出‘文化感知’研究方向，基于诠释推理重新构想人工智能，旨在解决低资源语言公平性能等问题，产出有文化意义的结果，为印度自然语言处理下一阶段研究提供方向并助力更强大、包容的印度基础模型发展。
英文摘要：
As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization. AI is seen as a ''double-edged sword'' where on the one hand, it can enable access and inclusion for a large population, on the other, it can homogenize worldviews and exclude underrepresented languages and worldviews. In this paper, we try to characterize this problem by addressing the extensive characteristic nature of Indian linguistics and the way they closely connect to cultural practices and worldview. We then perform a longitudinal survey of how Natural Language Processing (NLP) techniques have evolved in this space, tracing the historical development of Indic NLP, covering key milestones, methodological shifts, and resource creation efforts. In addition, the paper also examines the structural and sociolinguistic characteristics of Indian languages, such as rich morphology, complex scripts and grammar rules, diglossia, and large dialectal variation, and explains how these create unique challenges for building AI foundation models. We then discuss the growing role of Indic foundation models and analyze how these models address these long-standing resource and representation gaps. Finally, we propose a research direction called 'Culture Sensing', which re-imagines AI based on hermeneutic reasoning. Culture Sensing aims to address open problems such as ensuring equitable performance across low-resource languages and producing outputs that are culturally meaningful. By bringing together past work, current techniques, and emerging trends, this paper outlines research directions that can guide the next phase of Indic NLP and contribute to the development of more robust and inclusive Indic foundation models.
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。
