IndiVec: An Exploration of Leveraging Large Language Models for Media
  Bias Detection with Fine-Grained Bias Indicators
Luyang Lin Lingzhi Wang Xiaoyan Zhao Jing Li Kam-Fai Wong
This study focuses on media bias detection, crucial in today's era of
influential social media platforms shaping individual attitudes and opinions.
In contrast to prior work that primarily relies on training specific models
tailored to particular datasets, resulting in limited adaptability and subpar
performance on out-of-domain data, we introduce a general bias detection
framework, IndiVec, built upon large language models. IndiVec begins by
constructing a fine-grained media bias database, leveraging the robust
instruction-following capabilities of large language models and vector database
techniques. When confronted with new input for bias detection, our framework
automatically selects the most relevant indicator from the vector database and
employs majority voting to determine the input's bias label. IndiVec excels
compared to previous methods due to its adaptability (demonstrating consistent
performance across diverse datasets from various sources) and explainability
(providing explicit top-k indicators to interpret bias predictions).
Experimental results on four political bias datasets highlight IndiVec's
significant superiority over baselines. Furthermore, additional experiments and
analysis provide profound insights into the framework's effectiveness.