Papers with cross-lingual knowledge transfer
JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models’ Detection of Human risky health behavior Content in Jirai Community (2026.eacl-long)
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Yunze Xiao, Tingyu He, Lionel Z. Wang, Yiming Ma, Xingyu Song, Xiaohang Xu, Mona T. Diab, Irene Li, Ka Chung Ng
| Challenge: | a cross-lingual dataset captures a transnational cultural phenomenon . risky health behaviors (RHB) are often linked to complex mental health conditions . |
| Approach: | They present the first cross-lingual dataset that captures a transnational cultural phenomenon . their dataset of more than 15,000 annotated social media posts forms the core of JiraiBench . |
| Outcome: | The study shows that cultural context can be more influential than linguistic similarity . the study also shows that the Japanese prompts better handle Chinese content . |
Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)
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| Challenge: | Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages. |
| Approach: | They fine-tuned large language models (4B–671B parameters) on Arabic and evaluated zero-shot reading comprehension on Semitic languages and non-Semitic controls. |
| Outcome: | The results show that models with weak baselines improve across all languages, whereas strong-baseline models show only marginal gains regardless of language family. |
Large Language Models are Limited in Out-of-Context Knowledge Reasoning (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. |
| Approach: | They evaluated a dataset with seven representative OCKR tasks to assess their OCKr capabilities. |
| Outcome: | The model's OCKR abilities are limited regardless of whether the knowledge is trained in a separate or adjacent training setting. |
Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer (2024.findings-naacl)
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| Challenge: | Existing methods to adapt pretrained Large Language Models to new lower-resource languages are limited to English. |
| Approach: | They propose to combine cross-lingual instruction-tuning with additional monolingual pretraining to adapt LLMs to new lower-resource languages. |
| Outcome: | The proposed model is the first open-source instruction-following LLM for Estonian . the proposed model improves commonsense reasoning and multi-turn conversation capabilities . |
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs (2026.eacl-long)
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Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi
| Challenge: | Existing studies on unlearning in multilingual large language models focus on monolingual settings, typically English. |
| Approach: | They propose to use a multilingual data and concept unlearning model to investigate the problem . they extend benchmarks for factual knowledge and stereotypes into ten languages . |
| Outcome: | The proposed model is able to unlearning in 10 languages across five languages and resource levels. |
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling (C18-1)
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| Challenge: | Existing studies on multitask and multilingual joint learning focus on cross-task or cross-lingual knowledge transfer. |
| Approach: | They propose to divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and different languages. |
| Outcome: | The proposed method is able to support multi-task and multi-lingual learning using Japanese and English data sets with three different lexical utterance classification tasks. |
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages (2024.acl-long)
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| Challenge: | Contemporary large language models (LLMs) are pre-trained on multilingual corpora, but their performance lags behind in most languages compared to a few resource-rich languages. |
| Approach: | They propose a method that leverages the internal capabilities of large language models on resource-rich languages to enhance multilingual performance. |
| Outcome: | The proposed method improves multilingual performance while minimizing impact on original performance in resource-rich languages. |
LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs (2026.acl-long)
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Pei-Fu Guo, Yun-Da Tsai, Chun-Chia Hsu, Kai-Xin Chen, Ya An Tsai, Kai-Wei Chang, Nanyun Peng, Mi-Yen Yeh, Shou-De Lin
| Challenge: | Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either from genuine transfer or from prior exposure during pre-training. |
| Approach: | They propose a pipeline to isolate and measure cross-lingual knowledge transfer by identifying self-contained, time-sensitive knowledge entities from real-world domains and generating factual questions. |
| Outcome: | The proposed pipeline analyzes multiple LLMs across five languages and shows that cross-lingual transfer is strongly influenced by linguistic distance and often asymmetric across language directions. |
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches address key factors that influence multilingual ICL, but they do not integrate them into the model. |
| Approach: | They propose a method that quantifies and optimally balances three factors for improved example selection. |
| Outcome: | Experiments on mCSQA and TYDI show that the proposed method outperforms existing methods. |