AI RESEARCH
Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries
arXiv CS.CL
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ArXi:2505.05406v3 Announce Type: replace News headlines and summaries shape how events are interpreted through selective emphasis and omission, a phenomenon commonly referred to as framing. Large language models are now routinely used to generate such content, yet existing evaluation frameworks largely overlook this dimension. We