PlurVA-LLM-2026 Shared Task Track-1: Pluralistic Value Alignment in LLMs via Multilingual Fine-Tuning and Threshold Calibration

arXiv:2609.32382 · cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.CL

Submitted: 2026-09-26

Updated: 2026-10-08

Comments: 11 pages, 6 figures, 6 tables, Accepted paper at the first workshop on Pluralistic Value Alignment of LLMs @ AACL-IJCNLP 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 27× in this paper · explained in 1 episode
• Accuracy — 18× in this paper · explained in 1 episode
• Calibration — 12× in this paper · explained in 7 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• MaxMin-RLHF: Alignment with Diverse Human Preferences
• PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
• Survey on Publicly Available Sinhala Natural Language Processing Tools and Research
• D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios
• Pancasila-Dilemmas: Evaluating Large Language Models on Indonesian Human Value Dilemmas Grounded in Pancasila
• LKValues: Aligning Large Language Models with Sri Lankan Societal Values
• A Roadmap to Pluralistic Alignment
• Diverging Preferences: When do Annotators Disagree and do Models Know?

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