BiAxisBias: Evaluating LLM Bias Beyond a Single Prompt and a Single Explanation
LLM bias scores can depend on audit design. We introduce BiAxisBias, a prespecified audit varying task, role, perspective, sentiment, and wording over 200 stereotype statements while retaining forced Selection and Rationale as separate protocol readouts. Its main matrix spans eight LLMs and 401 templates (641,600 responses). Across five equivalent questions, 17.1% of 1,600 model-statement pairs change Selection. With three observations per unit in both arms, instability averages 10.5% across all ten three-wording subsets, versus 6.3% for three identical calls. Across four controlled task paradigms, 9/28 model pairs reverse; a seven-model factorial sensitivity identifies task-by-sentiment ...
| Authors | Jialing Gan, Junhao Dong, Songze Li |
|---|---|
| Primary category | cs.CL |
| Published | 9 May 2026 |
| Last updated at source | 17 August 2026 |
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