BBQ is a new benchmark dataset showing that QA models often default to social stereotypes, achieving up to 3.4 points higher accuracy when the correct answer aligns with bias.
Identifying and Reducing Gender Bias in Word-Level Language Models
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A methodological framework detects subtle group-associated linguistic biases in LLM outputs by generating controlled synthetic minimal pairs, abstracting n-grams, and ranking high-signal fragments with a PMI variant for expert review.
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BBQ: A Hand-Built Bias Benchmark for Question Answering
BBQ is a new benchmark dataset showing that QA models often default to social stereotypes, achieving up to 3.4 points higher accuracy when the correct answer aligns with bias.
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Contrastive Analysis of Linguistic Representations in Large Language Model Outputs through Structured Synthetic Data Generation and Abstracted N-gram Associations
A methodological framework detects subtle group-associated linguistic biases in LLM outputs by generating controlled synthetic minimal pairs, abstracting n-grams, and ranking high-signal fragments with a PMI variant for expert review.