arXiv Computation and Language September 21, 2026 By Reem I. Masoud, Martin Ferianc, Philip Treleaven, Miguel Rodrigues

Cultural Alignment in Large Language Models Using Soft Prompt Tuning

Read the original on arXiv Computation and Language →

The paper proposes a method for culturally aligning large language models (LLMs) using soft prompt tuning optimized via Differential Evolution (DE). Unlike traditional fine‑tuning or reinforcement learning, this approach keeps model weights frozen and requires no preference data, instead leveraging aggregated survey scores from Hofstede's Value Survey Module (VSM13). Experiments on four countries and four instruction‑tuned models show that DE‑optimized prompts reduce cultural discrepancy, improve agreement with the World Values Survey, and are preferred in blinded pairwise evaluations by LLM judges.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Aug 21

DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

arXiv:2509. 08022v3 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic variation.

By Yao Liang, Dongcheng Zhao, Feifei Zhao, Guobin Shen, Yuwei Wang, Dongqi Liang, Yi Zeng

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Computation and Language

Sep 2

Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs

arXiv:2609.00565v1 Announce Type: cross Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively...

By Jingshen Zhang, Shaoyang Xu, Wenxuan Zhang

llms fine-tuning benchmarks safety

arXiv AI

Jun 18

Steerable Cultural Preference Optimization of Reward Models

arXiv:2606. 18606v1 Announce Type: cross Abstract: It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community.

By Minsik Oh, Advit Deepak, Sophie Wu, Douwe Kiela, Ekaterina Shutova

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 23

Prompt Programming for Cultural Bias and Alignment of Large Language Models

arXiv:2603. 16827v2 Announce Type: replace Abstract: Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that misalign with target populations.

By Maksim Eren, Eric Michalak, Brian Cook, Johnny Seales Jr

llms nlp safety

arXiv AI

Sep 30

Population Fidelity: Evaluating Population Representativeness in LLMs

The paper introduces Population Fidelity, an evaluation framework for assessing how well large language models (LLMs) represent human population attitudes. It focuses on three dimensions: group-level accuracy, between-group variation, and the structure of that variation. Using the framework, the authors replicate a prior study on machine bias and test cultural fine-tuning, finding that while fine-tuning improves overall alignment, it does not enhance representation of within-population differences.

By Neemias B. da Silva, Martin Lukk, Ali Sutani, Abhishek Moturu, Harris Yang, Daniel Silver, Matt Ratto, Thiago H. Silva

llms fine-tuning safety

arXiv Computation and Language

Oct 19

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation

arXiv:2605.12515v3 Announce Type: replace Abstract: Multilingual large language models (MLLMs) often answer culturally ambiguous questions inconsistently across languages, even for a fixed user perso...

By Lucas Resck, Isabelle Augenstein, Anna Korhonen

llms safety

One email a morning, machine-written

Related stories

DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs

Steerable Cultural Preference Optimization of Reward Models

Prompt Programming for Cultural Bias and Alignment of Large Language Models

Population Fidelity: Evaluating Population Representativeness in LLMs

Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation