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DiSCo：衡量 LLM 文化偏好偏差的分布优先引导与先验评估框架
该 arXiv 预印本提出 DiSCo，一种分布优先的强制选择评估框架，通过四级上下文梯度（C0–C3）分离 LLM 的默认文化先验与上下文驱动的适应行为。作者基于 BLEnD 构建覆盖 12 种文化的 304 条 DiSCo-Bench，评测 6 个指令微调 LLM，发现默认文化先验高度集中，UK 与 US 合计约占全部选择的 35%（仅占 12 种文化中的 2 种）；提示式引导持续扩大高资源与低资源文化之间的选择差距，而注入显式文化事实带来的分布变化可忽略，表明文化偏好偏差无法仅靠提示个性化消除。
AI推荐理由 arXiv 预印本，提出分布优先的强制选择评估框架 DiSCo，用四级上下文梯度（C0–C3）分离 LLM 的默认文化先验与上下文驱动的适应能力，并基于 BLEnD 构建 304 条、覆盖 12 种文化的 DiSCo-Bench，评测 6 个指令微调模型。具体新信息：默认先验高度集中，UK 与 US 合计约占 35% 的选择（仅占 12 种文化中的 2 种）；提示式引导反而扩大高/低资源文化间的选择差距；注入显式文化事实对分布扰动可忽略，说明文化偏好偏差难以仅靠提示个性化解决。局限为预印本、样本规模有限，结论待同行评审与更大规模复现。
70
正文
Computer Science > Computation and Language
arXiv:2609.10253 (cs)
[Submitted on 9 Sep 2026]
Title:DiSCo: A Distribution-First Steering and Cultural Prior Evaluation Framework for Measuring Cultural Preference Bias in LLMs
Authors:Bhuvan Arora, Devesh Saraogi, Sravya Varada, Dhruv Kumar
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Abstract:Large language models (LLMs) are increasingly deployed in globally used assistants, yet their default choices in culturally grounded everyday situations can systematically favour some cultures over others, affecting localisation, user trust, and equitable behaviour. Existing cultural benchmarks evaluate accuracy against a single "correct" answer, making it difficult to characterise an LLM's cultural preference prior when multiple culturally grounded responses are all valid; they also conflate default preferences with context-driven adaptation. We propose DiSCo, a distribution-first forced-choice evaluation framework that isolates default cultural priors and tests steerability via a four-level context gradient (C0--C3). Using DiSCo-Bench (304 items) derived from BLEnD spanning 12 cultures, we evaluate six diverse instruction-tuned LLMs. Default priors are heavily concentrated, with UK and US together absorbing approximately 35\% of all selections despite representing only 2 of 12 cultures. Most critically, prompt-based steering consistently widens the selection gap between high- and low-resource cultures, and injecting explicit cultural facts produces negligible distributional disruption, confirming that cultural preference bias cannot be resolved through prompt-based personalisation alone.
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Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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arXiv:2609.10253 [cs.CL]
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arXiv:2609.10253v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.10253
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arXiv-issued DOI via DataCite (pending registration)
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From: Bhuvan Arora [view email]
[v1]
Wed, 9 Sep 2026 14:40:08 UTC (5,682 KB)
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