Multilingual LLM Evaluation

• Multilingual evaluation of LLMs is the systematic assessment of models’ performance, reasoning, and safety across varied languages and cultural contexts.
• It employs diverse methodologies including task-based benchmarks, translation pipelines, and human and automated adjudication to ensure robust and fair comparisons.
• Empirical studies reveal substantial performance gaps between English and low-resource languages, highlighting the need for balanced training and culturally aware model design.

Multilingual evaluation of LLMs refers to the systematic assessment of these models’ abilities to understand, generate, reason, and align with human values across multiple natural languages and cultural contexts. As LLMs are increasingly deployed globally, the demand has intensified for evaluation frameworks that extend beyond English and high-resource languages. Ensuring equitable language support, cultural sensitivity, safety, and robust cross-lingual reasoning in LLMs requires a multifaceted approach combining linguistic, functional, cultural, and safety-oriented evaluation methods.

1. Taxonomy of Multilingual LLM Evaluation

Recent systematic surveys (Zhu et al., 17 Nov 2024) outline that the evaluation of multilingual LLMs encompasses multiple interdependent layers:

• Tokenizer Evaluation: Metrics such as fertility (average number of subwords per word) and parity (ratio of token lengths for corresponding content in different languages) are used to assess intrinsic bias or inefficiency in splitting words from diverse scripts.
• Task-Based and Benchmark-Based Evaluation: Holistic frameworks (e.g., MEGA, BenchMAX (Huang et al., 11 Feb 2025), MuBench (Han et al., 24 Jun 2025), MMLU-ProX (Xuan et al., 13 Mar 2025), P-MMEval (Zhang et al., 14 Nov 2024), and GlotEval (Luo et al., 5 Apr 2025)) provide broad coverage across tasks like natural language understanding, commonsense reasoning, machine translation, code generation, summarization, and instruction following. Datasets such as XNLI, XQuAD, MLQA, MMLU variants, BELebele, and XL-Sum are commonly used.
• Cultural, Pragmatic, and Safety Evaluation: New benchmarks (e.g., OMGEval (Liu et al., 21 Feb 2024), MultiPragEval (Park et al., 11 Jun 2024), LinguaSafe (Ning et al., 18 Aug 2025), PolygloToxicityPrompts (Jain et al., 15 May 2024), MCEval (Huang et al., 13 Jul 2025), and domain-specific frameworks) assess cultural adaptation, pragmatic inference, toxicity, fairness, and safety.
• Functional and Proxy Evaluation: Functional evaluation methods move beyond static prompts, focusing on dynamic and verifiable outputs (e.g., CL-GSM Symbolic, CL-IFEval (Ojewale et al., 25 Jun 2025), MUG-Eval (Song et al., 20 May 2025)).
• Meta-Evaluation: MM-Eval (Son et al., 23 Oct 2024) directly assesses the reliability and fairness of evaluator LLMs themselves across languages.
• Intrinsic, Representation, and Interpretability-Oriented Evaluation: Probing internal model representations, neuron activation (e.g., Disentangling Language and Culture (Ying et al., 30 May 2025)), and alignment with intended behavior across languages.

This taxonomy reflects the breadth and complexity of modern multilingual LLM evaluation.

2. Methodologies and Benchmark Design

Modern frameworks emphasize diverse methodologies for robust multilingual assessment:

• Data Alignment and Translation: Large-scale benchmarks such as MuBench (Han et al., 24 Jun 2025), BenchMAX (Huang et al., 11 Feb 2025), and MMLU-ProX (Xuan et al., 13 Mar 2025) employ machine and LLM-assisted translation pipelines, with expert post-editing to ensure semantic, terminological, and cultural fidelity. Parallel data construction enables fair, direct cross-lingual comparison.
• Human and Automated Adjudication: Combination of multi-stage human annotation (e.g., BenchMAX’s three native-speaking annotators per item) and LLM-based adjudicators (e.g., GPT-4 in OMGEval (Liu et al., 21 Feb 2024), GPT-4o in MMLU-ProX (Xuan et al., 13 Mar 2025), or MM-Eval (Son et al., 23 Oct 2024)) for scalable and comparative judgment.
• Task Diversification: Benchmarks span multiple capabilities — from reading comprehension (Belebele, XNLI, XQuAD), code generation (mHumanEval (Raihan et al., 19 Oct 2024)), and summarization (XL-Sum) to instruction following, mathematical and science reasoning (MGSM, GPQA), long context understanding, tool use, and cultural knowledge (OMGEval, MCEval, MultiPragEval).
• Prompt and Configuration Control: Experiments compare English-only vs. native language prompts, chain-of-thought vs. direct answering, varying low-rank adaptation or quantization for parameter-efficient fine-tuning (MAPLE (Aggarwal et al., 15 Jan 2024)), and systematically evaluate the effects on performance.
• Translation Quality Assurance: Use of metrics (COMET-KIWI, BLEU, BERTScore) to select best candidate translations, combined with back-translation and human validation.
• Statistical Analysis and Utility Metrics: Use of paired-sample T-tests (P-MMEval (Zhang et al., 14 Nov 2024)), utility scoring, and metrics such as Multilingual Consistency (MLC in MuBench) to assess not only correctness but coherence across language versions.
• Functional and Conversational Evaluation: Dynamic, verifiable outputs (MUG-Eval (Song et al., 20 May 2025)), functional instruction-following, or code generation tasks supplement static-corpus accuracy assessment.

3. Empirical Insights: Performance, Gaps, and Robustness

Multilingual evaluation has surfaced key empirical phenomena:

• Persistent Gaps Between English and Non-English: Evaluations across aligned datasets consistently show higher accuracy, reasoning, and safety alignment in English and other high-resource languages compared to medium- and low-resource languages (Han et al., 24 Jun 2025, Xuan et al., 13 Mar 2025, Huang et al., 11 Feb 2025, Zhang et al., 14 Nov 2024). For example, the GAP metric in BenchMAX quantifies the delta between English and other languages, and MMLU-ProX documents up to 24.3% accuracy gaps between high- and low-resource languages.
• Impact of Model Scale and Architecture: Larger models (e.g., Qwen2.5-72B, GPT-4o) robustly outperform smaller ones across languages, but simply increasing size is insufficient to close low-resource deficits (Xuan et al., 13 Mar 2025, Huang et al., 11 Feb 2025). Parameter-efficient finetuning, with careful selection of rank and quantization (MAPLE (Aggarwal et al., 15 Jan 2024)), can substantially improve performance on low-resource languages and bring open models closer to proprietary baselines.
• Sensitivity to Prompting, Translation, and Training Distribution: Performance is influenced by native-language prompting (Zhang et al., 14 Nov 2024), translation consistency (Thellmann et al., 11 Oct 2024), and the distribution of pretraining/fine-tuning data. Cultural-linguistic synergy (alignment between language and cultural context) yields performance boosts (Ying et al., 30 May 2025). Translation and prompting strategy choices introduce variability, and human-in-the-loop review reduces systematic errors.
• Model Robustness and Real-World Generalization: Dynamic and functional benchmarks expose drops in instruction-following and reasoning not captured by static benchmarks. For example, functional benchmarks (CL-GSM Symbolic, CL-IFEval) highlight performance drops up to 24% between static and functional evaluations even in high-resource languages (Ojewale et al., 25 Jun 2025).
• Safety, Bias, and Cultural Understanding: Safety alignment and toxicity robustness vary across languages and domains (Ning et al., 18 Aug 2025, Jain et al., 15 May 2024). Cultural inclusion, fairness, and bias assessments (MCEval (Huang et al., 13 Jul 2025), OMGEval (Liu et al., 21 Feb 2024)) show that English-centric improvements may not generalize — in some cases, boosting English performance reduces fairness or cultural aptitude in native scenarios.

4. Specialized Evaluation Dimensions

Key contributions have introduced specialized evaluation dimensions:

Dimension	Benchmarks	Purpose/Metric
Cross-Lingual Consistency	MuBench (MLC) (Han et al., 24 Jun 2025)	Consistency of model outputs across languages, regardless of correctness
Pragmatic Inference	MultiPragEval (Park et al., 11 Jun 2024)	Inference under Gricean maxims; nuanced contextual understanding
Code Generation	mHumanEval (Raihan et al., 19 Oct 2024), BenchMAX	Pass@1, cross-lingual prompt → code mapping in 25+ PLs, 200+ NLs
Cultural Awareness/Bias	MCEval (Huang et al., 13 Jul 2025), OMGEval	Causal question rephrasing (counterfactual/confounder) to isolate model robustness
Safety/Toxicity	PolygloToxicityPrompts (Jain et al., 15 May 2024), LinguaSafe (Ning et al., 18 Aug 2025)	Multilingual toxicity, safety alignment, oversensitivity analysis
Meta-Evaluation	MM-Eval (Son et al., 23 Oct 2024)	Discriminator performance, fairness, and language-resource discrimination

These innovations provide deeper insights into linguistic, cultural, and contextual gaps that remain opaque to traditional accuracy or BLEU-based scoring.

5. Challenges, Limitations, and Future Directions

Multilingual LLM evaluation faces several persistent challenges:

• Resource Imbalance and Bias: Imbalanced training data yields significant performance and representation gaps. Future work is directed toward more balanced pretraining, targeted data augmentation, and modular adaptation (such as adapters for low-resource transfer) (Aggarwal et al., 15 Jan 2024).
• Translation and Cultural Localization: Even high-quality machine translations can introduce subtle errors or bias, particularly with domain terminology, idioms, or gendered constructs (Thellmann et al., 11 Oct 2024). Controlled, human-in-the-loop localization is necessary for reliable evaluation.
• Robustness and Reproducibility: Variability in model output can be introduced by prompting, batching, and hardware/software differences. Ensuring evaluation framework reproducibility is identified as an area for standardization (Thellmann et al., 11 Oct 2024).
• Meta-Evaluator Limitations: The assessment of LLMs as judges reveals reduced discrimination in low-resource settings, with over-assignment of middle-ground scores and fairness gaps (Son et al., 23 Oct 2024).
• Safety and Oversensitivity: Multilingual safety alignment is not guaranteed; models may underperform or “over-restrict” in medium- and low-resource contexts (Ning et al., 18 Aug 2025).
• Functional Generalization: Static benchmarks may overestimate generalization; real-world conversational, instructional, and code tasks expose underperformance not captured in standard benchmarks (Ojewale et al., 25 Jun 2025, Song et al., 20 May 2025).

Anticipated research directions include expansion of language coverage to underrepresented and code-switched varieties, culturally aware data augmentation, design of new translation or localization metrics, interpretability-oriented neuron probing, and the use of aligned meta-evaluation frameworks for model selection and tuning.

6. Implications for Model Development and Deployment

The ongoing evolution of multilingual LLM evaluation directly informs model development and deployment strategies:

• Systematic evaluation on benchmarks like BenchMAX, MuBench, MMLU-ProX, and GlotEval supports model selection and tracking progress toward equitable language coverage.
• Multilingual safety evaluation (LinguaSafe, PolygloToxicityPrompts) highlights security vulnerabilities, ensuring safer deployments in culturally and linguistically diverse contexts.
• Dual- and functional evaluation frameworks reveal the importance of cultural-linguistic synergy and the necessity of designing models that go beyond English-centric assumptions (Ying et al., 30 May 2025).
• Empirical studies show that advanced finetuning (e.g., parameter-efficient methods, domain-specific tuning, rank/quantization settings) can significantly improve non-English, low-resource, and domain-specialized performance.
• Released datasets, code, and leaderboards associated with these benchmarks provide replicable, scalable platforms for the global research community.

7. Summary Table: Major Benchmarks and Their Key Attributes

Benchmark	Domain Focus	Languages	Key Evaluation Characteristics
BenchMAX (Huang et al., 11 Feb 2025)	Advanced tasks, code, reasoning, long context	17	Multi-way, post-edited, domain translation challenge
MuBench (Han et al., 24 Jun 2025)	NLU, reasoning, QA, consistency	61	Fully aligned, semantic/cultural checks, MLC metric
MMLU-ProX (Xuan et al., 13 Mar 2025)	Reasoning, knowledge	13	Multi-stage translation, meticulous expert review
OMGEval (Liu et al., 21 Feb 2024)	Open-ended generation, cultural awareness	5	Human localization, GPT-4 as adjudicator
LinguaSafe (Ning et al., 18 Aug 2025)	Safety, helpfulness, oversensitivity	12	Multidimensional safety, severity-weighted confusion
MultiPragEval (Park et al., 11 Jun 2024)	Pragmatics, contextual inference	4	Gricean maxims, contextual/literal distinction
MUG-Eval (Song et al., 20 May 2025)	Conversational, proxy evaluation	30	Language-agnostic, task success rate, no LLM-as-judge
P-MMEval (Zhang et al., 14 Nov 2024)	Multitask, parallel, instruction, MT, code	10	Paired t-test utility, parallel sampling
GlotEval (Luo et al., 5 Apr 2025)	Machine translation, classification, et al.	1500+	Intrinsic/extrinsic metrics, prompt templating

This table encapsulates the breadth of multilingual evaluation methodologies and their respective coverage.

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Multilingual evaluation of LLMs continues to mature rapidly, driven by comprehensive benchmarks that probe not only understanding, reasoning, and code generation, but also cultural sensitivity, safety, fairness, and functional generalization. State-of-the-art frameworks emphasize parallel data, cross-lingual consistency, cultural roots, and robust empirical protocols, revealing both progress and ongoing gaps in multilingual AI. Research in this domain is foundational for the development and deployment of truly global, equitable, and reliable language technologies.