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A bias depth score is introduced that measures both how strongly a model prefers its top answer under direct prompting and whether that answer survives scenario reframing and shows that Deep biases are consistently harder to remove than Shallow biases.
The findings suggest that the relationship between culture and figurative language, though conceptually natural, is not straightforwardly captured through fine-tuning alone, and that fine-tuning reinforces experiential cultural knowledge while destabilizing historically grounded factual knowledge.
This work proposes to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective and introduces EXP - the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.
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