GPF-LiveNews: A Streaming Evaluation Protocol for Group-Conditioned Framing in Large Language Models
arXiv:2605.28848v1 Announce Type: cross Abstract: 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,
The rapid deployment and increasing societal impact of LLMs necessitate more robust and dynamic evaluation methods for bias and framing, especially as these models move into real-time applications.
This protocol addresses a critical gap in LLM evaluation, moving beyond static benchmarks to assess how models frame emerging events for diverse audiences, directly impacting trust and ethical deployment.
The introduction of a streaming evaluation protocol for group-conditioned framing provides a continuous, real-time mechanism to audit LLM outputs, allowing for quicker identification and mitigation of biases.
- · AI ethicists
- · Regulatory bodies
- · LLM developers investing in ethical AI
- · News organizations
- · LLM developers ignoring bias
- · Static evaluation benchmark providers
- · Propaganda networks leveraging LLMs
Immediate awareness of biased or misframing LLM outputs in real-time applications.
Increased pressure on LLM developers to integrate dynamic bias mitigation techniques into their deployment pipelines.
Enhanced public trust in AI systems due to transparent and continuous auditing of their outputs, or a significant challenge to their widespread adoption if biases are consistently revealed.
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Read at arXiv cs.AI