Cross-Lingual Consistency in Language Models

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12 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Cross-lingual Self-Consistency for Multilingual Reasoning with Language Models
May 31, 2026Ahmed Elhady, Eneko Agirre, Mikel ArtetxeMultilingual Language Models Cross-Lingual Reasoning in Language Models
HiTZ Center, University of the Basque Country (UPV/EHU) · Reka AI
XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks
May 29, 2026Purvam Jain, Preethi Jyothi, Vihari Piratla +1Multilingual Language Model Evaluation Cross-Lingual Reasoning in Language Models
Google DeepMind · Indian Institute of Technology Bombay · International Centre for Theoretical Sciences, Tata Institute of Fundamental Research
Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR
May 28, 2026Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +3Multilingual Language Model Evaluation Cross-Lingual Reasoning in Language Models
Indian Institute of Science Education and Research, Bhopal, India · Microsoft Corporation · UNSW Sydney, Australia
Brain-LLM Alignment Tracks Training Data, Not Typology
May 21, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuMultilingual Language Models Cross-Lingual Representation Alignment
The University of Hong Kong Hong Kong, China · Stellaris AI Limited Hong Kong, China
Cross-Lingual Response Consistency in Large Language Models: An ILR-Informed Evaluation of Claude Across Six Languages
Apr 29, 2026Camelia BalutaMultilingual Language Models Multilingual Language Model Evaluation
Independent Researcher | Former Faculty, Defense Language Institute Foreign Language Center · ILR/OPI Assessment Specialist
Multilingual Refusal Alignment for Safer Large Language Models
Apr 24, 2026Aleksandra Krasnodębska, Wojciech Kusa, Aldo LipaniMultilingual Language Model Evaluation LLM Alignment
NASK National Research Institute, Warsaw, Poland · University College London, London, UK
Lost in Translation: Do LVLM Judges Generalize Across Languages?
Apr 21, 2026Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Mir Tafseer Nayeem +5VLM Evaluation Multilingual VLM Evaluation
York University · University of Alberta · Nanyang Technological University +1
Imperative Interference: Social Register Shapes Instruction Topology in Large Language Models
Mar 26, 2026Tony MasonPragmatic Reasoning in Language Models Cross-Lingual Consistency in Language Models
the University of British Columbia · the Georgia Institute of Technology
One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
Sep 25, 2025Sualeha Farid, Jayden Lin, Zean Chen +2Multilingual Language Model Evaluation Moral Reasoning in Language Models
University of Michigan, Ann Arbor, USA
'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection
Date pendingFawzia Zehra (Fuzzy), Kara-Isitt, Sonal Khosla +1Multilingual Language Model Evaluation Language Model Safety Evaluation
Brunel University of London, UK · Hasso Plattner Institute Potsdam, Germany

May 31, 2026, cs.CLJ/K move · Enter open · S save

Cross-lingual Self-Consistency for Multilingual Reasoning with Language Models

Ahmed Elhady, Eneko Agirre, Mikel Artetxe

HiTZ Center, University of the Basque Country (UPV/EHU) · Reka AI

Multilingual Language Models Cross-Lingual Reasoning in Language Models RL for Language Model Reasoning Cross-Lingual Consistency in Language Models Cross-Lingual Transfer

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Despite expanding their multilingual coverage, the advanced reasoning capabilities of LLMs remain largely confined to a few high-resource languages like English. To address this, we propose an unsupervised Reinforcement Learning (RL) approach to enhance multilingual reasoning by enforcing cross-lingual self-consistency: the principle that a model should produce the same final answer for equivalent problems in different languages. Existing methods are limited by the scarcity of multilingual reasoning data and show weak generalization to unseen languages. Our approach requires neither gold answers nor parallel data, and it achieves average gains of up to 21.7% on MGSM across 10 languages. In addition, our method demonstrates strong generalization, with an 18.2% mean improvement on MGSM languages unseen during training, and up to 6.2% gain on 3 out-of-distribution benchmarks. These results show the potential of consistency-based methods to improve the multilingual capabilities of LLMs without requiring supervised data.

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