Decoding the Diversity: A Review of the Indic AI Research Landscape

本综述论文全面概述了印度语系大型语言模型(LLM)研究方向。印度语系包括印度、巴基斯坦、孟加拉国、斯里兰卡、尼泊尔和不丹等国家的语言。这些语言拥有丰富的文化和语言遗产，全球有超过15亿人口使用。随着自然语言处理(NLP)应用在不同语言中的市场潜力和需求不断增长，印度语系生成式应用为研究带来了独特的挑战和机遇。本文深入探讨了印度语系生成模型的最新进展，提供了研究方向分类，列出了84篇最近的出版物。本文调查的研究方向包括LLM开发、对现有LLM进行微调、语料库开发、基准测试和评估，以及特定技术、工具和应用的出版物。我们发现，出版物中的研究人员强调了有限的数据可用性、标准化的缺乏以及印度语系语言的特殊语言复杂性所带来的挑战。本文旨在为研究者和从事NLP领域的实践者提供有价值的资源，特别是那些专注于印度语系的研究者，并为这些语言的更精确、高效的LLM应用的开发做出贡献。

Indic languages have a rich cultural and linguistic heritage and are spoken by over 1.5 billion people worldwide, but there is limited research on large language model (LLM) development for these languages. This paper aims to provide a comprehensive overview of recent advancements in Indic generative modeling and contribute to the development of more accurate and efficient LLM applications for Indic languages.

The paper provides a taxonomy of research directions for Indic generative modeling, including LLM development, fine-tuning existing LLMs, development of corpora, benchmarking and evaluation, as well as publications around specific techniques, tools, and applications. The paper emphasizes the challenges associated with limited data availability, lack of standardization, and the peculiar linguistic complexities of Indic languages.

The paper tabulates 84 recent publications in the field of Indic generative modeling, and provides a valuable resource for researchers and practitioners working in the field of NLP, particularly those focused on Indic languages. The paper highlights the need for more standardized corpora and evaluation metrics for Indic languages. The paper also emphasizes the importance of considering the cultural and linguistic nuances of Indic languages in developing LLM applications.

Recent related work in this field includes 'A Survey of the State of the Art in Word Embeddings for Indic Languages' and 'Indic NLP Library: Overview and Insights'.