arXiv cs.CL· Shantipriya Parida, Debasish Dhal, Sonal Khosla, Sambit Sekhar, Muhammed Abdur Rahmaan Kamaldeen·· 13 小时前AI 评分18
Acharya 2.0：面向印地语自学科目的 RAG 驱动 AI 导师
Acharya 2.0: An AI Tutor for Self-Learning Subjects in Hindi
AI 导读
论文提出 Acharya 2.0，一个基于 RAG 的 AI 导师应用，为印地语学生提供学术辅助。该客户端-服务器 Web 应用整合多项功能，帮助学生访问并与教育内容交互。研究指出 RAG 技术此前多限于英语，在印度语言中应用有限。
正文 · 原文
Abstract:Text processing is crucial for low-resource languages as it enables the development of tools and technologies that bridge the digital divide and provide equitable access to information and services. By empowering low-resource languages with robust text processing, we can preserve linguistic diversity, enhance education, improve communi- cation, and foster inclusivity in a rapidly digitizing world. Due to the lack of sufficient annotated datasets and lin- guistic resources, it is challenging to develop accurate and reliable language technologies for these low-resource languages. Large Language Models (LLMs) have attracted sig- nificant attention with their performance and the value they produce. However, with the ever-evolving language, adapting LLMs for a specific task is time-consuming and resource-intensive. Thus, Retrieval-Augmented Genera- tion (RAG) has overcome this limitation by augmenting LLMs with external knowledge, thereby improving their efficiency and extensive task-specific adaptation. Most applications of the RAG technique have been limited to English and has seen limited application in In- dic languages. This paper introduces the RAG-powered AI-Tutor application, Acharya 2.0, designed to produce academic assistance to Hindi-speaking students. The client-server-based web application integrates many fea- tures to support students in accessing and interacting with educational content.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.25192 [cs.CL] |
| (or arXiv:2610.25192v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.25192 arXiv-issued DOI via DataCite |
Submission history
From: Debasish Dhal [view email]
[v1]
Sun, 18 Oct 2026 11:24:39 UTC (2,386 KB)
[v2]
Wed, 21 Oct 2026 15:37:24 UTC (2,386 KB)
来源：arXiv cs.CL · arxiv.org