Research Summary: Who Judges the Frame? Auditing Multimodal LLM Judges for News Framing Across Event-Level Perspectives

Large Language Models (LLMs), including multimodal variants, are increasingly utilized for analyzing news framing, sentiment, and perspective in media. However, their use as measurement instruments presents a significant methodological challenge, as their outputs may be influenced by model-specific tendencies, prompt design, and inherent socio-cultural or linguistic assumptions, rather than solely reflecting content properties.

Why it matters

The increasing reliance on AI models for sophisticated data analysis, particularly in areas like media content and public discourse, necessitates robust validation. Understanding and mitigating the inherent biases and limitations of these analytical tools is crucial for ensuring the reliability and integrity of insights derived from large-scale data processing, impacting decision-making in various sectors.

Key insights

• News coverage of significant global events is influenced by both the reported content and the framing conveyed through text, images, and their combination.
• Multimodal LLMs are being adopted as scalable tools for analyzing framing, sentiment, and perspective in large multimodal media datasets.
• A key methodological challenge arises from the potential for LLM outputs to reflect inherent model biases, prompt design choices, or embedded socio-cultural assumptions, rather than providing purely objective analysis of content.
• The research focuses on auditing LLMs when used as analytical instruments for large-scale framing and perspective analysis in multimodal contexts.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2610.00071