# Disentangling Language and Culture for Evaluating Multilingual Large Language Models

arXiv: https://arxiv.org/abs/2505.24635 · HTML: https://mlsanity.com/paper/2505.24635/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 16 of 16)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://mlsanity.com/paper/2402.10588/) (2024)
- [How do Large Language Models Handle Multilingualism?](https://mlsanity.com/paper/2402.18815/) (2024)
- [SeaLLMs -- Large Language Models for Southeast Asia](https://mlsanity.com/paper/2312.00738/) (2023)
- [On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons](https://mlsanity.com/paper/2404.02431/) (2024)
- [SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages](https://mlsanity.com/paper/2407.19672/) (2024)
- [Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs](https://mlsanity.com/paper/2406.09265/) (2024)
- [Intrinsic Test of Unlearning Using Parametric Knowledge Traces](https://mlsanity.com/paper/2406.11614/) (2024)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia](https://mlsanity.com/paper/2502.06298/) (2025)
- [Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric](https://mlsanity.com/paper/2504.07440/) (2025)
- [Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts](https://mlsanity.com/paper/2409.11056/) (2024)

## Built on by (most cited first, 16 of 16)

- [Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settings](https://mlsanity.com/paper/2509.24506/) (2025)
- [Language of Thought Shapes Output Diversity in Large Language Models](https://mlsanity.com/paper/2601.11227/) (2026)
- [Leveraging Wikidata for Geographically Informed Sociocultural Bias Dataset Creation: Application to Latin America](https://mlsanity.com/paper/2603.10001/) (2026)
- [Neuron-Level Analysis of Cultural Understanding in Large Language Models](https://mlsanity.com/paper/2510.08284/) (2025)
- [Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation](https://mlsanity.com/paper/2605.12515/) (2026)
- [CARTE: A Benchmark for Mapping Language Model Knowledge Across France](https://mlsanity.com/paper/2606.01995/) (2026)
- [Beyond Instrumental and Substitutive Paradigms: Introducing Machine Culture as an Emergent Phenomenon in Large Language Models](https://mlsanity.com/paper/2601.17096/) (2026)
- [Macaron: Controlled, Human-Written Benchmark for Multilingual and Multicultural Reasoning via Template-Filling](https://mlsanity.com/paper/2602.10732/) (2026)
- [Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective](https://mlsanity.com/paper/2602.17283/) (2026)
- [Mimir: Large-scale Multilingual Concept Modeling](https://mlsanity.com/paper/2605.25263/) (2026)
- [When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models](https://mlsanity.com/paper/2605.30481/) (2026)
- [Beyond Alignment: Value Diversity as a Collective Property in Multicultural Agent Systems](https://mlsanity.com/paper/2606.05985/) (2026)
- [The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs](https://mlsanity.com/paper/2606.07422/) (2026)
- [Demographic Prompting at Scale: When More Attributes Hurt LLM--Human Agreement](https://mlsanity.com/paper/2607.10590/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)
- [Belief-Trajectory Energy: Measuring the Path to a Prediction](https://mlsanity.com/paper/2610.05114/) (2026)

## Abstract (arXiv)

This paper introduces a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of LLMs. By decomposing the evaluation along the dimensions of linguistic medium and cultural context, this framework enables a nuanced analysis of LLMs' ability to process questions within both native and cross-cultural contexts cross-lingually. Extensive evaluations are conducted on a wide range of models, revealing a notable "CulturalLinguistic Synergy" phenomenon, where models exhibit better performance when questions are culturally aligned with the language. This phenomenon is further explored through interpretability probing, which shows that a higher proportion of specific neurons are activated in a language's cultural context. This activation proportion could serve as a potential indicator for evaluating multilingual performance during model training. Our findings challenge the prevailing notion that LLMs, primarily trained on English data, perform uniformly across languages and highlight the necessity of culturally and linguistically model evaluations. Our code can be found at https://yingjiahao14. github.io/Dual-Evaluation/.