arXiv:2505.05406v3 Announce Type: replace Abstract: 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 introduce Frame In, Frame Out (FIFO), the first large-scale benchmark for measuring framing presence in LLM-generated news summaries, grounded in the widely used XSum dataset. FIFO combines 15,499 jury-annotated examples with 320 expert-labeled instances ($
The proliferation of Large Language Models (LLMs) in content generation necessitates new frameworks to evaluate their nuanced societal impact beyond superficial metrics.
As LLMs become ubiquitous in news summarization, understanding and measuring their inherent framing bias is critical for maintaining information integrity and mitigating algorithmic manipulation of public perception.
The introduction of a specialized benchmark like Frame In, Frame Out (FIFO) provides the first large-scale, systematic method to assess framing bias, shifting the focus of LLM evaluation beyond accuracy to ethical implications.
- · AI ethics researchers
- · News organizations
- · Audiences desiring unbiased information
- · LLM developers ignoring bias
- · Platforms deploying unverified LLMs
- · Propagandists
LLM developers will be pressured to incorporate debiasing techniques into their models for news generation.
Public awareness of algorithmic framing bias will increase, leading to greater scrutiny of LLM-generated content.
New regulations or industry standards may emerge requiring measurable framing bias disclosures for AI-powered media tools.
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Read at arXiv cs.CL