Reflexive annotating elicits intersectional and positional metadata from crowd workers to make AI alignment annotations more situated and less assumed-neutral.
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Simple supervision improves LLM distributional alignment with diverse population groups on three datasets, with evaluation across multiple models and prompts providing a benchmark.
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"Label from Somewhere": Reflexive Annotating for Situated AI Alignment
Reflexive annotating elicits intersectional and positional metadata from crowd workers to make AI alignment annotations more situated and less assumed-neutral.
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Improving the Distributional Alignment of LLMs using Supervision
Simple supervision improves LLM distributional alignment with diverse population groups on three datasets, with evaluation across multiple models and prompts providing a benchmark.