SCOPE is a new large-scale dataset of counterfactual prompt pairs for evaluating fairness and stereotype sensitivity in LLMs across 1,438 topics, nine bias dimensions, 1,536 groups, and four communicative intents.
Bias and unfairness in information retrieval systems: New challenges in the llm era,
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Sensitive prompts serve as an early-warning signal for fairness risks in LLMs by eliciting responses that often miss ethical or contextual implications.
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SCOPE: A Dataset of Stereotyped Prompts for Counterfactual Fairness Assessment of LLMs
SCOPE is a new large-scale dataset of counterfactual prompt pairs for evaluating fairness and stereotype sensitivity in LLMs across 1,438 topics, nine bias dimensions, 1,536 groups, and four communicative intents.
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Bias Ahead: Sensitive Prompts as Early Warnings for Fairness in Large Language Models
Sensitive prompts serve as an early-warning signal for fairness risks in LLMs by eliciting responses that often miss ethical or contextual implications.