Summary of Vilbias: a Study Of Bias Detection Through Linguistic and Visual Cues , Presenting Annotation Strategies, Evaluation, and Key Challenges, by Shaina Raza et al.
VilBias: A Study of Bias Detection through Linguistic and Visual Cues , presenting Annotation Strategies, Evaluation, and Key Challenges
by Shaina Raza, Caesar Saleh, Emrul Hasan, Franklin Ogidi, Maximus Powers, Veronica Chatrath, Marcelo Lotif, Roya Javadi, Anam Zahid, Vahid Reza Khazaie
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) enables the analysis of complex challenges in multimodal content. This study introduces VLBias, a framework that leverages state-of-the-art LLMs and VLMs to detect linguistic and visual biases in news content. The framework uses a hybrid annotation method combining LLM-based annotations with human review for high-quality labeling while reducing costs and enhancing scalability. The evaluation compares the performance of SLMs and LLMs for both text and images, revealing that LLMs demonstrate superior accuracy in identifying subtle framing and text-visual inconsistencies. Additionally, empirical analysis shows that incorporating visual cues alongside textual data improves bias detection accuracy by 3 to 5%. This study explores the potential of LLMs, SLMs, and VLMs for detecting multimodal biases in news content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer models (LLMs, VLMs) to find biases in news articles. It makes a tool called VLBias that combines text and image analysis to detect these biases. The authors tested the tool with different kinds of articles and found that it works well. They also looked at how adding images helps or hurts the accuracy of detecting biases. Overall, the paper shows how these special computer models can help us better understand news articles. |
