This paper presents an overview of the PlurVA-LLM 2026 shared task on pluralistic value alignment across China, Indonesia, and Sri Lanka. To the best of our knowledge, this is the first shared task that aims to align a single large language model (LLM) to pluralistic values of multiple countries. We use 3 pluralistic value datasets: D2VBench (Hao et al., 2026) for China, Pancasila-Dilemmas (Supryadi et al., 2026) for Indonesia, and LKValues (Muthugala et al., 2026) for Sri Lanka. The shared task consists of two tracks: Track-1, a resource-constrained track (LLMs under 8B parameters) and Track-2, an open track without model-size constraints. We received registration from 20 teams across different countries. After finalization, we received 3 submissions for Track-1 and 2 submissions for Track-2. The final score is calculated as the average accuracy of the gold-standard answers across the three datasets. The submitted methods, including fine-tuning and prompt engineering, consistently surpasses the baselines. We believe that the results and findings from this shared task provide valuable insights and serve as a foundation for future research on pluralistic value alignment in LLMs. The dataset of this shared task is available in this link https://huggingface.co/datasets/TJUNLP/PlurVA-LLM-ST-2026.

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 datasets are publicly available at https://github.com/NextME14/LKValues.