Scholarlink

<p>Question answering systems (QAS) are designed to answer questions in natural language. The objective of these types of systems is to reduce the user’s effort to manually check the retrieved documents to find the answer to the query in natural language and to create an accurate answer to the user’s query. In recent years, with the emergence of Large Language Models (LLMs), these systems have evolved significantly across different languages. However, the development of QAS in low resource languages such as Persian, while progressing, still faces unique challenges. Development of these systems has become problematic in Persian language due to the lack of comprehensive processing tools, limited question answering datasets, and specific challenges of this language. The current study provides a brief explanation of these systems’ evolution from traditional architectures to LLM-based approaches, their classification, the challenges specific to Persian language, existing question-answering datasets and language models, and studies conducted concerning Persian QAS.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A review on persian question answering systems: from traditional to modern approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月13日

查看全文
  • Safoura Aghadavoud Jolfaei,
  • Azadeh Mohebi

摘要

Question answering systems (QAS) are designed to answer questions in natural language. The objective of these types of systems is to reduce the user’s effort to manually check the retrieved documents to find the answer to the query in natural language and to create an accurate answer to the user’s query. In recent years, with the emergence of Large Language Models (LLMs), these systems have evolved significantly across different languages. However, the development of QAS in low resource languages such as Persian, while progressing, still faces unique challenges. Development of these systems has become problematic in Persian language due to the lack of comprehensive processing tools, limited question answering datasets, and specific challenges of this language. The current study provides a brief explanation of these systems’ evolution from traditional architectures to LLM-based approaches, their classification, the challenges specific to Persian language, existing question-answering datasets and language models, and studies conducted concerning Persian QAS.