@inproceedings{supryadi-etal-2026-findings,
title = "Findings of the {P}lur{VA}-{LLM} 2026 Shared Task on Pluralistic Value Alignment Across {C}hina, {I}ndonesia, and {S}ri {L}anka",
author = "Supryadi and
Muthugala, Nethmi and
Hao, Siyi and
Qin, Aoxiang and
Zheng, Jingting and
Zhou, Li and
Zhang, Wenxuan and
Wang, Benyou and
Zhang, Hongming and
Branco, Ant{\'o}nio and
Xiong, Deyi",
editor = "Xiong, Deyi and
Branco, Ant{\'o}nio and
Zhang, Hongming and
Dong, Yue and
Wang, Benyou and
Zhang, Wenxuan and
Zhou, Li and
Zheng, Jingting",
booktitle = "Proceedings of the First Workshop on Pluralistic Value Alignment of {LLM}s",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.plurvallm-1.11/",
pages = "154--164",
ISBN = "979-8-89176-453-8",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Findings of the PlurVA-LLM 2026 Shared Task on Pluralistic Value Alignment Across China, Indonesia, and Sri Lanka
%A Muthugala, Nethmi
%A Hao, Siyi
%A Qin, Aoxiang
%A Zheng, Jingting
%A Zhou, Li
%A Zhang, Wenxuan
%A Wang, Benyou
%A Zhang, Hongming
%A Branco, António
%A Xiong, Deyi
%Y Xiong, Deyi
%Y Branco, António
%Y Zhang, Hongming
%Y Dong, Yue
%Y Wang, Benyou
%Y Zhang, Wenxuan
%Y Zhou, Li
%Y Zheng, Jingting
%A Supryadi
%S Proceedings of the First Workshop on Pluralistic Value Alignment of LLMs
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-453-8
%F supryadi-etal-2026-findings
%X 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.
%U https://aclanthology.org/2026.plurvallm-1.11/
%P 154-164
Markdown (Informal)
[Findings of the PlurVA-LLM 2026 Shared Task on Pluralistic Value Alignment Across China, Indonesia, and Sri Lanka](https://aclanthology.org/2026.plurvallm-1.11/) (Supryadi et al., PlurVA-LLM 2026)
ACL
- Supryadi, Nethmi Muthugala, Siyi Hao, Aoxiang Qin, Jingting Zheng, Li Zhou, Wenxuan Zhang, Benyou Wang, Hongming Zhang, António Branco, and Deyi Xiong. 2026. Findings of the PlurVA-LLM 2026 Shared Task on Pluralistic Value Alignment Across China, Indonesia, and Sri Lanka. In Proceedings of the First Workshop on Pluralistic Value Alignment of LLMs, pages 154–164, Hengqin, China. Association for Computational Linguistics.