News Articles provides crucial information about various events happening in the society but they unfortunately come with different kind of biases. These biases can significantly distort public opinion and trust in the media, making it essential to develop techniques to detect and address them. Previous works have majorly worked towards identifying biases in particular domains e.g., Political, gender biases. However, more comprehensive studies are needed to detect biases across diverse domains. Large language models (LLMs) offer a powerful way to analyze and understand natural language, making them ideal for constructing datasets and detecting these biases. In this work, we have explored various biases present in the news articles, built a dataset using LLMs and present results obtained using multiple detection techniques. Our approach highlights the importance of broad-spectrum bias detection and offers new insights for improving the integrity of news articles.

本研究解决了新闻文章中偏见未被广泛检测的问题，强调了综合性偏见检测的重要性。通过利用大型语言模型(LLMs)构建数据集，我们探索了新闻文章中存在的多种偏见，并使用多种检测技术获得了结果。这项工作为提高新闻文章的完整性提供了新见解。

解读新闻偏见：新闻文章中的多重偏见检测