GPF-LiveNews: A Streaming Evaluation Protocol for Group-Conditioned Framing in Large Language Models
Deployed language models are evaluated in a non-stationary environment: model versions, retrieval layers, safety systems, and real-world inputs all change over time. Static bias benchmarks remain useful, but they do not show how models frame newly emerging events for different prompted audiences. We introduce GPF-LIVENEWS, a streaming evaluation protocol and benchmark snapshot for auditing group-conditioned framing in open-ended LLM outputs. The protocol expands fresh BBC/Reuters news anchors across 42 identity labels and seven prompt families, then evaluates response bundles using semantic-sensitivity and sentiment-disparity signals. In a pilot over 12 monitoring runs and 23 hosted model...
| Authors | Mohd Ariful Haque, Fahad Rahman, Kishor Datta Gupta et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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