LKValues：使大语言模型与斯里兰卡社会价值观保持一致
LKValues: Aligning Large Language Models with Sri Lankan Societal Values
- TJUNLP Lab, School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院TJUNLP实验室）
- School of Mathematical and Computational Sciences, Massey University（梅西大学数学与计算科学学院）
- Department of Computer Science & Engineering, University of Moratuwa（莫拉图瓦大学计算机科学与工程系）
- Johns Hopkins University（约翰霍普金斯大学）
- School of Computing, University of Colombo（科伦坡大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对大语言模型价值对齐存在西方文化偏见问题，以斯里兰卡为例，提出LKValues资源套件，通过调查得出社会价值观，构建语料库和评估基准，经实验发现其能改善模型表现，为低资源国家价值对齐提供可复制流程。
AI中文摘要：
大语言模型（LLMs）的价值对齐在文化上偏向西方规范，导致像斯里兰卡这样具有独特文化动态的多语言社会中当地价值观被错误处理。现有基准忽略了斯里兰卡官方语言僧伽罗语中的情境化价值观。为弥补这一差距，我们提出LKValues，首个基于调查的斯里兰卡价值对齐资源套件。通过对205名受访者的三语调查，融合全球框架和大语言模型引出的本地结构，得出40个多数认可的社会价值观。利用这些价值观构建了包含15万个基于场景实例的僧伽罗语 - 英语新闻衍生指令语料库LKvaluesIT和1000个实例的价值敏感评估基准LKvaluesBench。我们用LKvaluesBench评估了一系列专有和开源权重的大语言模型，并对三个开源权重基础模型进行微调。实验表明，更新更大的大语言模型仍存在资源和文化价值对齐差距。LKValues微调改善了Qwen系列模型在英语和僧伽罗语方面的表现，减少了无效输出和跨语言差异，不过收益仍依赖于模型家族。这些突出了LKValues在嵌入斯里兰卡价值观方面的功效，为低资源、特定国家的多元价值对齐提供了可复制的流程。数据集可在指定网址公开获取。
英文摘要：
Value alignment of Large Language Models (LLMs) has been shown to be culturally biased toward Western norms. This results in the mishandling of local values in multilingual societies such as Sri Lanka that have their unique cultural dynamics. Existing benchmarks overlook Sri Lankan-contextualized values in its official language Sinhala, hindering culturally sensitive evaluation and fine-tuning. To bridge this gap, we propose LKValues, the first survey-grounded resource suite for Sri Lankan value alignment. From a trilingual survey of 205 respondents, blending adapted global frameworks and LLM-elicited local constructs, we derive 40 majority-endorsed societal values. Using these values, we construct LKvaluesIT, a Sinhala-English news-derived instruction corpus containing 150k scenario-based instances, and LKvaluesBench, a value-sensitive evaluation benchmark of 1,000 instances. We evaluate a set of proprietary and open-weight LLMs with LKvaluesBench. We fine-tune three open-weight base models (Qwen3.5-4B-Base, Qwen3.5-9B-Base, and Aya-Expanse-8B-Base). Our experiments show that newer and larger LLMs still exhibit low-resource and cultural value-alignment gaps. LKValues fine-tuning improves Qwen-family models in English and Sinhala, reducing invalid outputs and cross-lingual disparities, though gains remain model-family dependent. These highlight LKValues efficacy in embedding Sri Lankan values, offering a replicable pipeline for low-resource, country-specific pluralist value alignment. The dataset is publicly available at https://github.com/NextME14/LKValues.
