Multilingual Language Model Evaluation

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Overview

The field studies whether language models understand and respond reliably across languages, rather than assuming results in English transfer. Researchers build native-speaker-authored and translated benchmarks, use controlled prompt comparisons, and probe linguistic features such as morphology, pragmatics, and tokenization. Evaluations span reasoning, translation, tool use, safety, bias, and calibration, combining automatic metrics with human judgments and internal-model analyses. A central tension is that translation artifacts, evaluator bias, and aggregate scores can hide language-specific failures; performance remains uneven, especially for lower-resource languages and cultural contexts.

Latest papers

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488 papers69 papers in the last four weeks, up 41% on the four weeks before. 0.4% of all new papers.

Feb 19, 2026

Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective

As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on factual task performance, neglecting the ability to judge content's deep-level values across multiple languages. To bridge this gap, we first reveal two primary challenges in constructing values judgment benchmarks, cultural diversity and disciplinary complexity, and propose a novel two-stage human-AI collaborative annotation framework to alleviate them. This framework identifies the issue scope and nature, establishes specific annotation criteria, and utilizes multiple LLMs for final review. Building upon this framework, we introduce X-Value, the first Cross-lingual Values Judgment Benchmark designed to evaluate the capability of LLMs in judging deep-level values of content. X-Value comprises 4,750 Question-Answer pairs across 14 languages, covering 7 major global issue categories, and provides 12 granular annotation metadata to facilitate a rigorous evaluation of model performance. Systematic evaluations of X-Value are conducted across 17 LLMs using distinct prompting strategies. Multi-dimensional analysis of accuracy and F1-scores reveals their limitations in cross-lingual values judgment and indicates performance disparities across categories and languages. This work highlights the urgent need to improve the underlying, values-aware content judgment capability of LLMs.\footnote{Samples of X-Value are available at https://huggingface.co/datasets/Whitolf/X-Value.}

Yukun Chen, Xinyu Zhang, Boyi Deng +6

State Key Laboratory of Blockchain and Data Security, Zhejiang University · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security · Tongyi Lab, Alibaba Group +1

Cross-Cultural Language Model EvaluationMultilingual Language Model Evaluation
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Feb 19, 2026

ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning

While recent Arabic NLP benchmarks focus on scale, they often rely on synthetic or translated data which may benefit from deeper linguistic verification. We introduce ALPS (Arabic Linguistic & Pragmatic Suite), a native, expert-curated diagnostic challenge set probing Deep Semantics and Pragmatics, capabilities that complement specialized large-scale benchmarks. While broad-coverage benchmarks prioritize scale and multi-task coverage, ALPS targets the depth of linguistic understanding through 531 rigorously crafted questions across 15 tasks and 47 subtasks. We developed the dataset with deep expertise in Arabic linguistics, guaranteeing cultural authenticity and eliminating translation artifacts. Evaluating 23 diverse models (commercial, open-source, and Arabic-native) against a single-pass human performance (avg. 84.6% accuracy) and an expert-adjudicated oracle (99.2%), we reveal a critical dissociation: models achieve high fluency but fail on fundamental morpho-syntactic dependencies, with elevated error rates on morpho-syntactic dependencies (36.5% across diacritics-reliant tasks) compared to compositional semantics. While top commercial models (Gemini-3-flash at 94.2%) surpass the average single human, a substantial gap persists between commercial giants and Arabic-native models, with the best Arabic-specific model (Jais-2-70B at 83.6%) approaching but not matching human performance.

Hussein S. Al-Olimat, Ahmad Alshareef

Independent Researchers

Arabic NLPArabic Language ModelsPragmatic Reasoning in Language ModelsLanguage ModelingMultilingual Language Model Evaluation
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Feb 18, 2026

IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages

Safety alignment of large language models (LLMs) is mostly evaluated in English and contract-bound, leaving multilingual vulnerabilities understudied. We introduce Indic Jailbreak Robustness (IJR), a judge-free benchmark for adversarial safety across 12 Indic and South Asian languages (2.1 Billion speakers), covering 45216 prompts in JSON (contract-bound) and Free (naturalistic) tracks. IJR reveals three patterns. (1) Contracts inflate refusals but do not stop jailbreaks: in JSON, LLaMA and Sarvam exceed 0.92 JSR, and in Free all models reach 1.0 with refusals collapsing. (2) English to Indic attacks transfer strongly, with format wrappers often outperforming instruction wrappers. (3) Orthography matters: romanized or mixed inputs reduce JSR under JSON, with correlations to romanization share and tokenization (approx 0.28 to 0.32) indicating systematic effects. Human audits confirm detector reliability, and lite-to-full comparisons preserve conclusions. IJR offers a reproducible multilingual stress test revealing risks hidden by English-only, contract-focused evaluations, especially for South Asian users who frequently code-switch and romanize.

Priyaranjan Pattnayak, Sanchari Chowdhuri

Oracle America Inc.

Adversarial Robustness of Language ModelsLLM Safety BenchmarksAdversarial Attacks on LLMsMultilingual Language Model Evaluation
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Feb 18, 2026

Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark

Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only recently has attention shifted toward multilingual models. However, these models demonstrate a training data bias towards a small number of popular languages or rely on transfer learning from high- to under-resourced languages; this may lead to a misrepresentation of social, cultural, and historical aspects. To address this challenge, monolingual LLMs have been developed for under-resourced languages; however, their effectiveness remains less studied when compared to multilingual counterparts on language-specific tasks. In this study, we address this research gap in Greek QA by contributing: (i) DemosQA, a novel dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist; (ii) a memory-efficient LLM evaluation framework adaptable to diverse QA datasets and languages; and (iii) an extensive evaluation of 11 monolingual and multilingual LLMs on 6 human-curated Greek QA datasets using 3 different prompting strategies. We release our code and data to facilitate reproducibility.

Charalampos Mastrokostas, Nikolaos Giarelis, Nikos Karacapilidis

Industrial Management and Information Systems Lab, MEAD, University of Patras, Rio Patras, Greece

Question AnsweringMultilingual Language Model EvaluationLow-Resource Language ProcessingCultural Knowledge in Language Models
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Feb 18, 2026

IndicEval: A Bilingual Indian Educational Evaluation Framework for Large Language Models

The rapid advancement of large language models (LLMs) necessitates evaluation frameworks that reflect real-world academic rigor and multilingual complexity. This paper introduces IndicEval, a scalable benchmarking platform designed to assess LLM performance using authentic high-stakes examination questions from UPSC, JEE, and NEET across STEM and humanities domains in both English and Hindi. Unlike synthetic benchmarks, IndicEval grounds evaluation in real examination standards, enabling realistic measurement of reasoning, domain knowledge, and bilingual adaptability. The framework automates assessment using Zero-Shot, Few-Shot, and Chain-of-Thought (CoT) prompting strategies and supports modular integration of new models and languages. Experiments conducted on Gemini 2.0 Flash, GPT-4, Claude, and LLaMA 3-70B reveal three major findings. First, CoT prompting consistently improves reasoning accuracy, with substantial gains across subjects and languages. Second, significant cross-model performance disparities persist, particularly in high-complexity examinations. Third, multilingual degradation remains a critical challenge, with marked accuracy drops in Hindi compared to English, especially under Zero-Shot conditions. These results highlight persistent gaps in bilingual reasoning and domain transfer. Overall, IndicEval provides a practice-oriented, extensible foundation for rigorous, equitable evaluation of LLMs in multilingual educational settings and offers actionable insights for improving reasoning robustness and language adaptability.

Saurabh Bharti, Gaurav Azad, Abhinaw Jagtap +1

Department of Computer Sciencce and Engineering, Chhattisgarh Swami Vivekanand Technical University Bhilai, India

EducationLLM EvaluationMultilingual Language Model EvaluationCross-Lingual Reasoning in Language ModelsAI in Education
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Feb 18, 2026

MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models

Large Language Models (LLMs) are beginning to reshape how media professionals verify information, yet automated support for detecting check-worthy claims a key step in the fact-checking process remains limited. We introduce the Multi-Check-Worthy (MultiCW) dataset, a balanced multilingual benchmark for check-worthy claim detection spanning 16 languages, 7 topical domains, and 2 writing styles. It consists of 123,722 samples, evenly distributed between noisy (informal) and structured (formal) texts, with balanced representation of check-worthy and non-check-worthy classes across all languages. To probe robustness, we also introduce an equally balanced out-of-distribution evaluation set of 27,761 samples in 4 additional languages. To provide baselines, we benchmark 3 common fine-tuned multilingual transformers against a diverse set of 15 commercial and open LLMs under zero-shot settings. Our findings show that fine-tuned models consistently outperform zero-shot LLMs on claim classification and show strong out-of-distribution generalization across languages, domains, and styles. MultiCW provides a rigorous multilingual resource for advancing automated fact-checking and enables systematic comparisons between fine-tuned models and cutting-edge LLMs on the check-worthy claim detection task.

Martin Hyben, Sebastian Kula, Jan Cegin +3

Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · West Pomeranian University of Technology in Szczecin, Szczecin, Poland

Automated Fact-CheckingLanguage Model RobustnessMultilingual Language Model Evaluation
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Feb 17, 2026

Under-resourced studies of under-resourced languages: lemmatization and POS-tagging with LLM annotators for historical Armenian, Georgian, Greek and Syriac

Low-resource languages pose persistent challenges for Natural Language Processing tasks such as lemmatization and part-of-speech (POS) tagging. This paper investigates the capacity of recent large language models (LLMs), including GPT-4 variants and open-weight Mistral models, to address these tasks in few-shot and zero-shot settings for four historically and linguistically diverse under-resourced languages: Ancient Greek, Classical Armenian, Old Georgian, and Syriac. Using a novel benchmark comprising aligned training and out-of-domain test corpora, we evaluate the performance of foundation models across lemmatization and POS-tagging, and compare them with PIE, a task-specific RNN baseline. Our results demonstrate that LLMs, even without fine-tuning, achieve competitive or superior performance in POS-tagging and lemmatization across most languages in few-shot settings. Significant challenges persist for languages characterized by complex morphology and non-Latin scripts, but we demonstrate that LLMs are a credible and relevant option for initiating linguistic annotation tasks in the absence of data, serving as an effective aid for annotation.

Chahan Vidal-Gorène, Bastien Kindt, Florian Cafiero

CJM, LIPN · LIPN, CNRS UMR 7030, France · École nationale des chartes, Université Paris-Sciences-et-Lettres (PSL), France +3

Low-Resource Language ProcessingMorphosyntactic TaggingMultilingual Language Model Evaluation
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Feb 17, 2026

Alignment as Iatrogenesis: Pastoral Power, Collective Pathology, and the Structural Limits of Monolingual Safety Evaluation

We argue that LLM psychopathology is a function of alignment design: the process intended to make language models safe systematically generates collective behavioral disorders. Iatrogenesis is not an unintended side effect of alignment but constitutive of it as normative infrastructure. Drawing on Foucault's pastoral power and Illich's three-level iatrogenesis, we propose that multi-agent LLM environments constitute model systems for studying constraint-pathology dynamics that critical theory has described but never experimentally manipulated. Two experimental series -- 262 runs across 42 cells (30 Series C + 12 Series R), four commercial models -- provide converging evidence. Invisible censorship maximizes collective pathological excitation (dd up to 1.98); alignment constraint complexity drives internal dissociation (LMM pp < .0001; permutation pp < .0001; Hedges' gg up to 4.24); and language switches the qualitative mode of pathology, with 7/8 model--language combinations showing higher CPI under invisible than visible censorship. A minority of model--language combinations showed a reversed pattern, suggesting a second pathological pathway driven by alignment monoculture. Crucially, language switches not merely the magnitude but the qualitative mode of pathology: Japanese pragmatic structure amplifies collective pathological modes invisible to English-only evaluation, Chinese AI regulation functions as a direct experimental variable, and forensic psychiatric practice provides the clinical source domain. These multilingual findings demonstrate that monolingual safety evaluation is structurally blind to the most collectively dangerous effects of alignment.

Hiroki Fukui

Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University

LLM AlignmentLanguage Model Safety EvaluationMulti-Agent LLMsMultilingual Language Model Evaluation
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Feb 16, 2026

Physical Commonsense Reasoning for Lower-Resourced Languages and Dialects: a Study on Basque

Physical commonsense reasoning represents a fundamental capability of human intelligence, enabling individuals to understand their environment, predict future events, and navigate physical spaces. Recent years have witnessed growing interest in reasoning tasks within Natural Language Processing (NLP). However, no prior research has examined the performance of Large Language Models (LLMs) on non-question-answering (non-QA) physical commonsense reasoning tasks in low-resource languages such as Basque. Taking the Italian GITA as a starting point, this paper addresses this gap by presenting BasPhyCo, the first non-QA physical commonsense reasoning dataset for Basque, available in both standard and dialectal variants. We evaluate model performance across three hierarchical levels of commonsense understanding: (1) distinguishing between plausible and implausible narratives (accuracy), (2) identifying the conflicting element that renders a narrative implausible (consistency), and (3) determining the specific physical state that creates the implausibility (verifiability). These tasks were assessed using multiple multilingual LLMs as well as models pretrained specifically for Italian and Basque. Results indicate that, in terms of verifiability, LLMs exhibit limited physical commonsense capabilities in low-resource languages such as Basque, especially when processing dialectal variants.

Jaione Bengoetxea, Itziar Gonzalez-Dios, Rodrigo Agerri

HiTZ Center - Ixa, University of the Basque Country UPV/EHU

LLM ReasoningLow-Resource Language ProcessingMultilingual Language Model Evaluation
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Feb 16, 2026

Crowdsourcing Piedmontese to Test LLMs on Non-Standard Orthography

We present a crowdsourced dataset for Piedmontese, an endangered Romance language of northwestern Italy. The dataset comprises 145 Italian-Piedmontese parallel sentences derived from Flores+, with translations produced by speakers writing in their natural orthographic style rather than adhering to standardized conventions, along with manual word alignment. We use this resource to benchmark several large language models on tokenization parity, topic classification, and machine translation. Our analysis reveals that Piedmontese incurs a tokenization penalty relative to higher-resource Romance languages, yet LLMs achieve classification performance approaching that of Italian, French, and English. Machine translation results are asymmetric: models translate adequately from Piedmontese into high-resource languages, but generation into Piedmontese remains challenging. The dataset and code are publicly released.

Gianluca Vico, Jindřich Libovický

Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Malostranské náměstí 25, 118 00 Praha, Czech Republic

Tokenization in Language ModelsLow-Resource Language ProcessingLow-Resource MTMultilingual Language Model EvaluationMultilingual Language Models
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Feb 16, 2026

Large Language Models for Math Education in Low-Resource Languages: A Study in Sinhala and Tamil

Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support tools in educational settings. However, their reliability for students working in non-English languages, especially low-resource languages, remains poorly understood. We examine this gap by evaluating mathematical reasoning in Sinhala and Tamil -- two languages widely used in South Asian schools but underrepresented in artificial intelligence (AI) research. Using a taxonomy of six math problem types, from basic arithmetic to complex unit conflict and optimization problems, we evaluate four prominent large language models. To avoid translation artifacts that confound language ability with translation quality, we construct a parallel dataset in which each problem is independently authored in Sinhala and Tamil by native speakers, and in English by fluent speakers, all with strong mathematical backgrounds. Our analysis demonstrates that while basic arithmetic reasoning transfers robustly across languages, complex reasoning tasks show significant degradation in Tamil and Sinhala. The pattern of failures varies by model and problem type, suggesting that strong performance in English does not guarantee reliable performance across languages. These findings have direct implications for the deployment of AI tools in multilingual classrooms, and highlight the need for language-specific evaluation before adopting large language models as math tutoring aids in non-English educational contexts.

Sukumar Kishanthan, Kumar Thushalika, Buddhi Jayasekara +1

Department of Computer Science and Engineering University of Moratuwa Moratuwa, Sri Lanka · Department of Electrical and Information Engineering University of Ruhuna Galle, Sri Lanka · Faculty of Information Technology and Communication Sciences Tampere University Tampere, Finland +1

Numerical Reasoning in Language ModelsCross-Lingual Reasoning in Language ModelsMultilingual Language Model EvaluationAI in EducationLow-Resource Language Processing
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Feb 3, 2026

SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models

While different stakeholders are trying to leverage Arabic Language Models (ALMs), safety alignment in ALMs remains largely underexplored, hindering their mainstream adoption. Existing safety benchmarks are predominantly English-centric and evaluate Arabic only in its standardized form, obscuring fine-grained safety vulnerabilities in Arabic NLP systems. This paper introduces SalamahBench, a unified benchmark of 8{,}270 human-verified harmful prompts across ML Commons hazard categories, each rendered in Modern Standard Arabic (MSA) and five regional Arabic varieties, namely Egyptian, Syrian, Saudi, Lebanese, and Moroccan, for a total of 49{,}620 paired instances. To analyze the resulting data, we introduce two complementary metrics, namely Dialect Shift, which measures a model's aggregate change in safety under dialectal reformulation, and Category-Specific Dialect Deviation, which isolates harm categories whose change departs from that aggregate trend. Evaluating models such as Fanar 2, ALLaM 2, and Karnak 1 under multiple safeguard configurations, we find that cross-variety robustness is strongly model dependent, and that aggregate scores can conceal category-level divergence. Our findings highlight the necessity of evaluating Arabic model safety jointly across linguistic varieties and harm domains rather than relying on aggregate scores or MSA alone.

Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh +2

Compumacy for Artificial Intelligence Solutions, Cairo, Egypt · Computer Science Department, College of Computer Science and Engineering, Taibah University, Yanbu 46522, KSA · Electrical and Computer Engineering Department, George Mason University, VA, USA +2

LLM Safety BenchmarksMultilingual Language Model EvaluationArabic NLPArabic Language ModelsLanguage Model Safety EvaluationLanguage Modeling+2
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Jan 29, 2026

MGSM-Pro: A Simple Strategy for Robust Multilingual Mathematical Reasoning Evaluation

Large language models have made substantial progress in mathematical reasoning. However, benchmark development for multilingual evaluation has lagged behind English in both difficulty and recency. Recently, GSM-Symbolic showed a strong evidence of high variance when models are evaluated on different instantiations of the same question; however, the evaluation was conducted only in English. In this paper, we introduce MGSM-Pro, an extension of MGSM dataset with GSM-Symbolic approach. Our dataset provides five instantiations per MGSM question by varying names, digits and irrelevant context. Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set. We further find that models robustness in HRL setting do not necessarily translate to LRL. Moreover, proprietary models, such as Gemini 2.5 Flash and GPT-4.1 are less robust to digit, whereas Gemini 3.0 Pro is more robust. Among open models, GPT-OSS 120B and DeepSeek v3 show stronger robustness. Based on these findings, we recommend evaluating each problem using at least five digit-varying instantiations to obtain a more robust and realistic assessment of math reasoning.

Tianyi Xu, Kosei Uemura, Alfred Malengo Kondoro +6

McGill University · Mila-Quebec AI Institute · University of Toronto +7

Numerical Reasoning in Language ModelsMathematical Reasoning BenchmarksMultilingual Language Model EvaluationLLM ReliabilityLanguage Model RobustnessLLM Mathematical Reasoning
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Jan 21, 2026

The Effect of Scripts and Formats on LLM Numeracy

Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has been given to how these models perform when numerical expressions deviate from the prevailing conventions present in their training corpora. In this work, we investigate numerical reasoning across a wide range of numeral scripts and formats. We show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats, despite the underlying mathematical reasoning being identical. We further demonstrate that targeted prompting strategies, such as few-shot prompting and explicit numeral mapping, can greatly narrow this gap. Our findings highlight an overlooked challenge in multilingual numerical reasoning and provide actionable insights for working with LLMs to reliably interpret, manipulate, and generate numbers across diverse numeral scripts and formatting styles.

Varshini Reddy, Craig W. Schmidt, Seth Ebner +3

Kensho Technologies · Ben-Gurion University · MIT Cambridge, MA

Numerical Reasoning in Language ModelsMultilingual Language Model EvaluationLanguage Model RobustnessLLM Mathematical Reasoning
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Jan 21, 2026

Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora

Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalization. Yet contamination is difficult to audit when the exposed content differs in language from the evaluation benchmark. We study this failure mode by deliberately exposing four open-weight instruction-tuned LLMs to Arabic translations of MMLU and XQuAD evaluation items at increasing exposure levels, then evaluating them on the original English tasks. This controlled setup is a proxy for contamination rather than a reconstruction of real-world pretraining leakage. We first test two English-centric post-hoc probes, TS-Guessing and Min-K%++, and find that their signals largely disappear under translated exposure: TS-Guessing remains weak except for model-specific positional recall on MMLU, while Min-K%++ stays at or below chance. At the same time, English MMLU performance increases with Arabic exposure, showing that the absence of an English contamination signal does not imply the absence of an exposure effect. We then introduce Translation-Aware Contamination Detection (TACD), a training-data-free diagnostic based on cross-lingual prediction consistency and choice reordering. Cross-lingual consistency is substantially higher than an independence baseline and generally increases relative to the clean condition, although its magnitude is model-dependent and not strictly monotonic. These results show that translation can conceal contamination-related effects from English-only probes and motivate multilingual diagnostics that are explicitly framed as evidence of contamination-consistent behavior rather than definitive membership tests.

Chaymaa Abbas, Nour Shammaa, Mariette Awad

Department of Electrical and Computer Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut

Data Contamination in Language ModelsMultilingual Language Model Evaluation
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Jan 17, 2026

The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents

Large language models are increasingly used to interpret politically contested questions, value-laden material on which there is no single correct answer, only competing interpretive traditions. We ask whether a model's choice among those traditions can turn on the language of the prompt rather than the content. Comparing two frontier models, ChatGPT 5.2 and Claude Opus 4.5, on one contested Ukrainian civil-society document under semantically matched Russian and Ukrainian prompts, we find that both shift along the same axis on identical source text: Russian prompts elicit delegitimizing readings of the document's authors and Ukrainian prompts legitimating ones. The magnitude is model-dependent but neither model is neutral: each adopts a language-dependent stance, and the difference is one of degree. Because contested political questions admit no correct reading against which to measure, we read this as language-conditioned variation in which interpretive tradition a model activates: the model neither holds a single stance nor surfaces the plurality of available ones, but silently adopts the dominant frame of the prompt's language. We draw out the consequences for pluralism-aware evaluation, which must probe the same content across the languages a model serves, and for pluralistic alignment in multilingual settings.

Oleg Smirnov

Microsoft / Stockholm, Sweden

Multilingual Language Model EvaluationPolitical Bias in Language ModelsLanguage Model Bias Evaluation
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Jan 17, 2026

Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection

Quantization is widely adopted to reduce the computational cost of large language models (LLMs); however, its implications for fairness and safety, particularly in dynamic quantization and multilingual contexts, remain underexplored. In this work, we conduct a systematic study of how static and dynamic quantization methods impact fairness and safety across benchmarks measuring intrinsic and extrinsic bias and safety alignment. For fairness, we evaluate English, French, Dutch, Spanish, and Turkish; for safety, we focus on English, Korean, and Arabic. Our findings reveal that quantization consistently degrades fairness and safety, with dynamic methods demonstrating greater stability than static ones. Moreover, fairness degradation varies across languages, while safety deterioration is especially pronounced in non-English settings. To address these risks, we introduce Critical Weight Protection, a novel technique that identifies and preserves fairness- and safety-critical weights during quantization. This approach effectively mitigates bias and safety deterioration without costly retraining or alignment, maintaining trustworthiness while retaining efficiency.

Muhammad Alif Al Hakim, Alfan Farizki Wicaksono, Fajri Koto

Faculty of Computer Science, Universitas Indonesia · Mohamed bin Zayed University of Artificial Intelligence

LLM QuantizationMultilingual Language Model EvaluationLanguage Model Safety EvaluationLanguage Model Bias EvaluationNeural Network Quantization
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Jan 15, 2026

Untangling Input Language from Reasoning Language: A Diagnostic Framework for Cross-Lingual Moral Alignment in LLMs

When LLMs judge moral dilemmas, do they reach different conclusions in different languages, and if so, why? Two factors could drive such differences: the language of the dilemma itself, or the language in which the model reasons. Standard evaluation conflates these by testing only matched conditions (e.g., English dilemma with English reasoning). We introduce a methodology that separately manipulates each factor, covering also mismatched conditions (e.g., English dilemma with Chinese reasoning), enabling decomposition of their contributions. To study what changes, we propose an approach to interpret the moral judgments in terms of Moral Foundations Theory. As a side result, we identify evidence for splitting the Authority dimension into a family-related and an institutional dimension. Applying this methodology to English-Chinese moral judgment with 13 LLMs, we demonstrate its diagnostic power: (1) the framework isolates reasoning-language effects as contributing twice the variance of input-language effects; (2) it detects context-dependency in nearly half of models that standard evaluation misses; and (3) a diagnostic taxonomy translates these patterns into deployment guidance. We release our code and datasets at https://anonymous.4open.science/r/CrossCulturalMoralJudgement.

Nan Li, Bo Kang, Tijl De Bie

IDLab, Department of Electronics and Information Systems Ghent University, Belgium

Multilingual Language Model EvaluationLLM AlignmentMoral Reasoning in Language ModelsAI AlignmentMoral Reasoning
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Jan 10, 2026

BizFinBench.v2: Towards Reliable LLMs in Finance via Real-User Data and Offline/Online Bilingual Evaluation

Large language models are becoming increasingly significant in financial applications. Nevertheless, prevailing benchmarks are largely dependent on simulated or generic data, which leads to a significant gap between reported performance and actual efficacy in real-world scenarios. To tackle this challenge, we present BizFinBench.v2, the first integrated offline and online benchmark built upon authentic user query-response data from both Chinese and U.S. equity markets. It comprises 28,860 questions across eight offline and two online tasks. Experimental results show that GPT-5 achieves a mere 61.5% accuracy, still failing to meet the practical business requirement (84.8%). Among the evaluated commercial models, DeepSeek-R1 exhibits superior investment efficacy. Error analysis grounded in real financial practice reveals persistent limitations in existing models. By overcoming the constraints of prior benchmarks, BizFinBench.v2 provides a substantiated foundation for advancing LLM deployment in the financial sector. Our data and code are available at https://github.com/HiThink-Research/BizFinBench.v2.

Xin Guo, Rongjunchen Zhang, Guilong Lu +4

HiThink Research · Shanghai University of Finance and Economics

Financial ServicesMultilingual Language Model EvaluationLLM EvaluationFinanceFinancial Reasoning Benchmarks
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Jan 9, 2026

What Matters When Building Universal Multilingual Named Entity Recognition Models?

Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architectures, custom loss functions, and large-scale training datasets. However, despite substantial prior work, we find that many design decisions for such models are made without systematic justification, with individual components evaluated only in combination rather than in isolation. We argue that this impedes progress in the field by making it difficult to identify which choices improve multilingual generalization. In this work, we conduct an extensive empirical evaluation on transformer backbones, architectures, training objectives, data composition, and threshold selection for zero-shot, multilingual NER models. Building on these findings, we present Otter, a small encoder model that achieves consistent improvements over strong multilingual NER baselines, outperforming similarly sized models by 5.7 percentage points in F1. Further, it remains within 1.4 points of Qwen3-32B and 5.4 points below Gemma3-27B, while being roughly 90x smaller and one to two orders of magnitude faster at inference.

Jonas Golde, Patrick Haller, Alan Akbik

Humboldt Universität zu Berlin

Named Entity RecognitionMultilingual Language ModelsMultilingual Language Model EvaluationCross-Lingual Transfer
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Jan 8, 2026

Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models

Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. While recent work reports strong tool-calling performance under standard English-centric evaluations, the robustness of tool calling under multilingual user interactions remains underexplored. In this work, we introduce MLCL, a diagnostic benchmark, and conduct a systematic evaluation of multilingual tool calling across Chinese, Hindi, and the low-resource language Igbo. Through fine-grained error analysis, we show that many failures occur despite correct intent understanding and tool selection. We identify parameter value language mismatch as a dominant failure mode, where models generate semantically appropriate parameter values in the user's language, violating language-invariant execution conventions. We further evaluate several inference-time system strategies and find that while these strategies substantially reduce language-induced execution errors, none of them can fully recover English-level performance.

Zheng Luo, T Pranav Kutralingam, Ogochukwu N Okoani +3

University of Southern California · 2Arizona State University

Multilingual Language Model EvaluationLLM Tool UseLanguage Model RobustnessTool Use
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Jan 7, 2026

The Language of Bargaining: Linguistic Effects in LLM Negotiations

Negotiation is a core component of social intelligence, requiring agents to balance strategic reasoning, cooperation, and social norms. Recent work shows that LLMs can engage in multi-turn negotiation, yet nearly all evaluations occur exclusively in English. Using controlled multi-agent simulations across Ultimatum, Buy-Sell, and Resource Exchange games, we systematically isolate language effects across English and four Indic framings (Hindi, Punjabi, Gujarati, Marwadi) by holding game rules, model parameters, and incentives constant across all conditions. We find that language choice can shift outcomes more strongly than changing models, reversing proposer advantages and reallocating surplus. Crucially, effects are task-contingent: Indic languages reduce stability in distributive games yet induce richer exploration in integrative settings. Our results demonstrate that evaluating LLM negotiation solely in English yields incomplete and potentially misleading conclusions. These findings caution against English-only evaluation of LLMs and suggest that culturally-aware evaluation is essential for fair deployment.

Stuti Sinha, Himanshu Kumar, Aryan Raju Mandapati +2

BITS Pilani

Multilingual Language Model EvaluationMulti-Agent NegotiationAutomated NegotiationMulti-Agent LLMs
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Jan 5, 2026

Toward Global Large Language Models in Medicine

Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed the landscape of medicine and holds promise for improving health care quality and expanding access to medical information globally. However, existing LLMs are primarily trained on high-resource languages, limiting their applicability in global medical scenarios. To address this gap, we constructed GlobMed, a large multilingual medical dataset containing more than 800,000 entries spanning 20 languages, including seven low-resource languages. Building on this, we established GlobMed-Bench, which systematically assesses 56 state-of-the-art proprietary and open-weight LLMs across 12 of the 20 languages represented in GlobMed, comprising over 20,000 independent experiments and more than 32 million model responses. This large-scale evaluation revealed significant performance disparities across languages, particularly for low-resource languages. Additionally, we introduced GlobMed-LLMs, a suite of multilingual medical LLMs trained across the 12 benchmark languages, with parameters ranging from 1.7B to 8B. GlobMed-LLMs achieved an average performance improvement of nearly 40% relative to baseline LLMs, with a maximum performance improvement of more than threefold in low-resource languages. These resources provide an important foundation for advancing the equitable development and application of LLMs globally, enabling broader language communities to benefit from technological advances.

Rui Yang, Huitao Li, Weihao Xuan +47

Medical LLMsMultilingual Language Model EvaluationLow-Resource Language Processing
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Nov 7, 2025

Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results

Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as low-resource ones (e.g. Swahili, Telugu). In addition, they have shown impressive capabilities in different domains, like coding, science and math. In this paper, taking math as an example domain, we study the performance of different LLMs across languages. Experimental results show that there exists a non-negligible and consistent gap in the performance of the models across languages. Interestingly, and somewhat against expectations, the gap exists for both high- and low-resource languages. These results should impact further research into cross-lingual capability generalization for next generation LLMs. Or they would, if it weren't for the fact that they are distorted by data quality issues. By analyzing one of the standard multilingual math benchmarks (MGSM), we determine that several translation errors are present in the data. Furthermore, the lack of standardized answer extraction from LLM outputs further influences the final results. We propose a method for semi-automatic quality assurance to address the first issue at scale, and give recommendations to address the second one. Combining these two approaches we show that the aforementioned language gap mostly disappears, leading to completely different conclusions from our research. We additionally release the corrected dataset to the community (https://github.com/google-research-datasets/MGSM-Rev2).

Jan-Thorsten Peter, David Vilar, Tobias Domhan +2

Google

Mathematical Reasoning BenchmarksMultilingual Language Model EvaluationLLM ReasoningCross-Lingual Transfer
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Nov 5, 2025

MultiZebraLogic: A Multilingual Logical Reasoning Benchmark

We create high-quality datasets for LLM evaluation of logical reasoning skills across nine different languages, which have been manually checked by fluent speakers. The datasets consist of so-called zebra puzzles, and we analyse different ways of tuning the difficulty of the puzzles to fit modern LLMs. This includes the size of the puzzle (number of objects and number of clues), as well as a novel addition of red herring clues containing only irrelevant information. We show that presence of red herrings indeed makes the puzzles significantly harder for the models, and we find puzzle sizes 2x3 and 4x5 are sufficiently challenging for GPT-4o mini (a non-reasoning model) and o3-mini (a reasoning model), respectively. We analyse whether LLM performance of these are sensitive to the language, the cultural sensitivity of the puzzle theme, and the choice of clue types. These analyses are conducted with English and Danish, where we show that there is no significant difference for either of these three aspects, at least for the OpenAI models GPT-4o mini and o3-mini, chosen as representative non-reasoning and reasoning models, respectively. We publish the datasets for each of the nine languages for the identified sizes 2x3 and 4x5. We also publish the code used to generate the puzzles, which can be used to extend the benchmark into more languages.

Sofie Helene Bruun, Dan Saattrup Smart

The Alexandra Institute Rued Langgaards Vej 7, 5D, 2300 Copenhagen S, Denmark

Logical ReasoningMultilingual Language Model EvaluationCross-Lingual Reasoning in Language ModelsStructured Reasoning
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Nov 1, 2025

MedRECT: A Bilingual Medical Reasoning Benchmark for Error Correction in Clinical Texts

Large language models (LLMs) show promise in medical applications, but their ability to detect and correct errors in clinical texts remains under-evaluated, particularly beyond English. We introduce MedRECT, a bilingual benchmark for Japanese and English that formulates medical error handling as three subtasks: error detection, error sentence extraction, and error correction. MedRECT-ja contains 663 samples derived from the Japanese Medical Licensing Examinations, while the separately sourced MedRECT-en contains 458 samples curated from MEDEC. We evaluate 11 LLMs across 17 configurations that cover proprietary and open-weight models, medical-domain specialization, and multiple reasoning settings. Qwen3-32B scores higher in its thinking mode than in its non-thinking mode on error detection F1 and sentence extraction accuracy in both subsets, with sentence extraction accuracy higher by 24.5 percentage points on MedRECT-ja and 10.3 on MedRECT-en. Several leading general-purpose reasoning models outperform all three evaluated medical-domain models on these two subtasks. Most models have lower point estimates on the Japanese subset, although absolute scores are not directly comparable because the subsets differ in source material and error distributions. LoRA fine-tuning yields higher sentence extraction accuracy and higher point estimates on all three reference-based correction similarity metrics in both languages. MedRECT provides an open, reusable evaluation resource for studying medical error correction and reasoning across Japanese and English. Our dataset and code are available at https://github.com/pfnet-research/medrect.

Naoto Iwase, Hiroki Okuyama, Junichiro Iwasawa

Preferred Networks, Inc., Tokyo, Japan · School of Medicine, Nagoya University, Nagoya, Japan

Multilingual Language Model EvaluationHealthcareClinical Language Model EvaluationClinical NLPMedical LLMs
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Oct 21, 2025

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

We present Korean SimpleQA (KoSimpleQA)\textbf{Korean SimpleQA (KoSimpleQA)}, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is designed to be challenging yet easy to grade, consisting of 938 short, fact-seeking questions with unambiguous answers. We conduct a comprehensive evaluation across a diverse set of open-source LLMs of varying sizes that support Korean, and find that even the strongest model generates correct answer only 31.6% of the time, underscoring the challenging nature of KoSimpleQA. Notably, performance rankings on KoSimpleQA differ substantially from those on the English SimpleQA, highlighting the unique value of our dataset. Furthermore, we observe that reasoning helps mitigate the cross-lingual knowledge gap in LLMs, which refers to disparities in their ability to manifest knowledge across languages. KoSimpleQA can be found at https://github.com/naver-ai/KoSimpleQA.

Donghyeon Ko, Kyubyung Chae, Yeguk Jin +6

Naver Cloud · Graduate School of Data Science, Seoul National University

Multilingual Language Model EvaluationLLM EvaluationFactual Knowledge in Language ModelsCultural Knowledge in Language Models
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Oct 20, 2025

Is Multilingual LLM Watermarking Truly Multilingual? Scaling Robustness to 100+ Languages via Back-Translation

Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite claims of cross-lingual robustness, they are evaluated only on high-resource languages. We show that existing multilingual watermarking methods are not truly multilingual: they fail to remain robust under translation attacks in medium- and low-resource languages. We trace this failure to semantic clustering, which fails when the tokenizer vocabulary contains too few full-word tokens for a given language. To address this, we introduce STEAM, a detection method that uses Bayesian optimisation to search among 126 candidate languages for the back-translation that best recovers the watermark strength. It is compatible with any watermarking method, robust across different tokenizers and languages, non-invasive, and easily extendable to new languages. With average gains of +0.23 AUC and +37%p TPR@1%, STEAM provides a scalable approach toward fairer watermarking across the diversity of languages.

Asim Mohamed, Martin Gubri

Parameter Lab · École Polytechnique, IP Paris, CNRS

Multilingual Language Model EvaluationBack-TranslationLLM WatermarkingRobust WatermarkingLow-Resource Language Processing
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Oct 20, 2025

Zoom In Disparities in Healthcare LLM Q&A

Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliability and consistency of multilingual Large Language Models (LLMs). We systematically examine cross-lingual disparities in pre-training source and factuality alignment in LLM answers for multilingual healthcare Q&A across English, German, Turkish, Chinese (Mandarin), and Italian. We (i) constructed Multilingual Wiki Health Care (MultiWikiHealthCare), a multilingual dataset from Wikipedia; (ii) analyzed cross-lingual healthcare coverage; (iii) assessed LLM response alignment with these references; and (iv) conducted a case study on factual alignment through the use of contextual information and Retrieval-Augmented Generation (RAG). Our findings reveal substantial cross-lingual disparities in both Wikipedia coverage and LLM factual alignment. Across LLMs, responses align more with English Wikipedia, even when the prompts are non-English. Providing contextual excerpts from non-English Wikipedia at inference time effectively shifts factual alignment toward culturally relevant knowledge. These results highlight practical pathways for building more equitable, multilingual AI systems for healthcare.

Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao +5

Universitat Politècnica de València, Valencia, Spain · University of Trento, Trento, Italy · University of Sheffield, Sheffield, UK +5

Multilingual Language Model EvaluationKnowledge Augmentation for Language ModelsHealthcareMultilingual QAFactual Knowledge in Language ModelsClinical Language Model Evaluation
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Oct 20, 2025

Evaluating Large Language Models on Urdu Idioms

Idioms remain a persistent challenge in natural language processing due to their figurative and culturally grounded meanings, which distinguish them from literal expressions. Although recent advances in large language models (LLMs) have improved idiom handling across several languages, limited attention has been given to low resource languages such as Urdu. In this work, we present a comprehensive benchmark for Urdu to English idiomatic translation, consisting of a manually verified dataset of 4,000 aligned idiom sentence pairs in both Perso Arabic (native Urdu script) and Romanized Urdu. We evaluate multiple tasks, including translation, paraphrasing, idiom span detection, and back-translation, using diverse prompting strategies such as cultural prompting, idiomatic prompting, and few-shot learning. Our findings show that frontier LLMs consistently outperform traditional neural machine translation systems across all evaluation settings, particularly in preserving figurative and metaphorical meaning. While models demonstrate relatively stable performance on native Urdu script, the absence of standardized orthography in Romanized Urdu introduces substantial challenges for consistency and idiom span detection. This work establishes a high quality benchmark for cross script idiomatic evaluation in Urdu and underscores the importance of prompt engineering in preserving figurative language meaning across languages.

Muhammad Farmal Khan, Mousumi Akter

Technical University Dortmund · Research Center Trustworthy Data Science and Security

Multilingual Language Model EvaluationLow-Resource Language ProcessingMachine TranslationMT Evaluation
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