Navigating Nuance: In Quest for Political Truth
This work addresses the challenge of political bias detection for mitigating misinformation and polarization, though it appears incremental as it matches rather than surpasses existing methods.
This study tackled the problem of detecting political bias in text by evaluating the Llama-3 (70B) language model on the Media Bias Identification Benchmark (MBIB) using a novel prompting technique. The approach achieved comparable performance to the state-of-the-art supervised ConvBERT model on this task.
This study investigates the several nuanced rationales for countering the rise of political bias. We evaluate the performance of the Llama-3 (70B) language model on the Media Bias Identification Benchmark (MBIB), based on a novel prompting technique that incorporates subtle reasons for identifying political leaning. Our findings underscore the challenges of detecting political bias and highlight the potential of transfer learning methods to enhance future models. Through our framework, we achieve a comparable performance with the supervised and fully fine-tuned ConvBERT model, which is the state-of-the-art model, performing best among other baseline models for the political bias task on MBIB. By demonstrating the effectiveness of our approach, we contribute to the development of more robust tools for mitigating the spread of misinformation and polarization. Our codes and dataset are made publicly available in github.