VilBias: A Study of Bias Detection through Linguistic and Visual Cues , presenting Annotation Strategies, Evaluation, and Key Challenges

• URL: http://arxiv.org/abs/2412.17052v3
• Date: Tue, 18 Feb 2025 22:01:51 GMT
• Title: VilBias: A Study of Bias Detection through Linguistic and Visual Cues , presenting Annotation Strategies, Evaluation, and Key Challenges
• Authors: Shaina Raza, Caesar Saleh, Emrul Hasan, Franklin Ogidi, Maximus Powers, Veronica Chatrath, Marcelo Lotif, Roya Javadi, Anam Zahid, Vahid Reza Khazaie,
• Abstract summary: VLBias is a framework that leverages state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs) to detect linguistic and visual biases in news content. We present a multimodal dataset comprising textual content and corresponding images from diverse news sources.
• Score: 2.2751168722976587
• License:
• Abstract: The integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) opens new avenues for addressing complex challenges in multimodal content analysis, particularly in biased news detection. This study introduces VLBias, a framework that leverages state-of-the-art LLMs and VLMs to detect linguistic and visual biases in news content. We present a multimodal dataset comprising textual content and corresponding images from diverse news sources. We propose a hybrid annotation framework that combines LLM-based annotations with human review to ensure high-quality labeling while reducing costs and enhancing scalability. Our evaluation compares the performance of state-of-the-art SLMs and LLMs for both modalities (text and images) and the results reveal that while SLMs are computationally efficient, LLMs demonstrate superior accuracy in identifying subtle framing and text-visual inconsistencies. Furthermore, empirical analysis shows that incorporating visual cues alongside textual data improves bias detection accuracy by 3 to 5%. This study provides a comprehensive exploration of LLMs, SLMs, and VLMs as tools for detecting multimodal biases in news content and highlights their respective strengths, limitations, and potential for future applications

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