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A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI

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arXiv:2605. 31021v1 Announce Type: new Abstract: Current alignment paradigms for generative artificial intelligence rely predominantly on monolithic benchmarking frameworks that reduce the plurality of human judgment to aggregated statistical baselines, thereby obscuring cultural, demographic, and contextual variability in evaluation.

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Authors: Atahan Karagoz
Released: 2026-06-01 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because A Persona-Based Evaluation Framework for Pluralistic Alignment in Generative AI overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, persona, under, alignment, eval, line, trained. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.31021

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