Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent?
- URL: http://arxiv.org/abs/2507.12838v1
- Date: Thu, 17 Jul 2025 06:55:15 GMT
- Title: Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent?
- Authors: Xi Ai, Mahardika Krisna Ihsani, Min-Yen Kan,
- Abstract summary: Cross-lingual consistency should be considered to assess cross-lingual transferability.<n>Code-switching training and cross-lingual word alignment objectives show the most promising results.
- Score: 28.76156047784995
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain the factuality of the model knowledge across languages, and preserve the parity of language model performance. We are thus interested in analyzing, evaluating, and interpreting cross-lingual consistency for factual knowledge. We examine code-mixed coreferential statements conveyed identical knowledge across languages to study cross-lingual knowledge consistency. We use some interpretability approaches to analyze the behavior of a model in cross-lingual contexts, discovering that multilingual models show different levels of consistency, subject to language families, linguistic factors, and a bottleneck in cross-lingual consistency on a particular layer. In addition, we evaluate common strategies aimed at improving multilingual performance to observe whether these strategies can improve knowledge consistency at the same time. While knowledge is not cross-lingual consistency in many cases, code-switching training and cross-lingual word alignment objectives show the most promising results, emphasizing the noteworthiness of cross-lingual alignment supervision and code-switching training for both multilingual performance and cross-lingual consistency enhancement.
Related papers
- Multilingual Self-Taught Faithfulness Evaluators [11.200203292660758]
Self-Taught Evaluators for Multilingual Faithfulness is a framework that learns exclusively from synthetic multilingual summarization data.<n>Our framework shows improvements over existing baselines, including state-of-the-art English evaluators and machine translation-based approaches.
arXiv Detail & Related papers (2025-07-28T12:01:59Z) - Evaluating Knowledge-based Cross-lingual Inconsistency in Large Language Models [16.942897938964638]
Large Language Models (LLMs) have shown exceptional performance in various Natural Language Processing (NLP) tasks.
Despite their successes, these models often exhibit significant inconsistencies when processing the same concepts across different languages.
This study focuses on three primary questions: the existence of cross-lingual inconsistencies in LLMs, the specific aspects in which these inconsistencies manifest, and the correlation between cross-lingual consistency and multilingual capabilities of LLMs.
arXiv Detail & Related papers (2024-07-01T15:11:37Z) - Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly [53.04368883943773]
Two approaches are proposed to address this, i.e., multilingual pretraining and multilingual instruction tuning.
We propose CLiKA to assess the cross-lingual knowledge alignment of LLMs in the Performance, Consistency and Conductivity levels.
Results show that while both multilingual pretraining and instruction tuning are beneficial for cross-lingual knowledge alignment, the training strategy needs to be carefully designed.
arXiv Detail & Related papers (2024-04-06T15:25:06Z) - Are Structural Concepts Universal in Transformer Language Models?
Towards Interpretable Cross-Lingual Generalization [27.368684663279463]
We investigate the potential for explicitly aligning conceptual correspondence between languages to enhance cross-lingual generalization.
Using the syntactic aspect of language as a testbed, our analyses of 43 languages reveal a high degree of alignability.
We propose a meta-learning-based method to learn to align conceptual spaces of different languages.
arXiv Detail & Related papers (2023-10-19T14:50:51Z) - Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models [7.478369203246005]
We study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs.<n>We propose a Ranking-based Consistency (RankC) metric to evaluate knowledge consistency across languages independently from accuracy.
arXiv Detail & Related papers (2023-10-16T13:19:17Z) - Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of
Multilingual Language Models [73.11488464916668]
This study investigates the dynamics of the multilingual pretraining process.
We probe checkpoints taken from throughout XLM-R pretraining, using a suite of linguistic tasks.
Our analysis shows that the model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones.
arXiv Detail & Related papers (2022-05-24T03:35:00Z) - Cross-lingual Lifelong Learning [53.06904052325966]
We present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm.
We provide insights into what makes multilingual sequential learning particularly challenging.
The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata.
arXiv Detail & Related papers (2022-05-23T09:25:43Z) - AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages
with Adversarial Examples [51.048234591165155]
We present AM2iCo, Adversarial and Multilingual Meaning in Context.
It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts.
Results reveal that current SotA pretrained encoders substantially lag behind human performance.
arXiv Detail & Related papers (2021-04-17T20:23:45Z) - Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting.
Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
arXiv Detail & Related papers (2021-03-24T16:20:02Z) - Cross-lingual Spoken Language Understanding with Regularized
Representation Alignment [71.53159402053392]
We propose a regularization approach to align word-level and sentence-level representations across languages without any external resource.
Experiments on the cross-lingual spoken language understanding task show that our model outperforms current state-of-the-art methods in both few-shot and zero-shot scenarios.
arXiv Detail & Related papers (2020-09-30T08:56:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.