How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual language models can learn generalisations useful for other languages . yet, it remains unclear to what extent and under which conditions these models benefit from multilingual data and cross-lingual sharing. |
| Approach: | They propose a training data attribution method to retrieve training samples from multilingual data that are most influential for test predictions in a given language. |
| Outcome: | The proposed method exploits the ability to learn generalisations useful for other languages on zero-shot cross-lingual transfer for many languages. |
Similar Papers
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning (2025.acl-long)
Copied to clipboard
Yangfan Ye, Xiaocheng Feng, Zekun Yuan, Xiachong Feng, Libo Qin, Lei Huang, Weitao Ma, Yichong Huang, Zhirui Zhang, Yunfei Lu, Xiaohui Yan, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | Existing fine-tuning approaches that focus on English-centric training corpora often introduce implicit cross-lingual alignment, overlooking the potential for more profound, latent-level cross-linguistic interactions. |
| Approach: | They propose a multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. |
| Outcome: | The proposed model outperforms vanilla SFT and offers a strong latent-level alternative to data-level augmentation methods. |
How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited. |
| Approach: | They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages. |
| Outcome: | The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages. |
Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
| Outcome: | The proposed setup improves on the state-of-the-art for a total of 15 languages. |
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual text classification leverage text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Approach: | They propose to combine a neural machine translator and a text classifier trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Outcome: | The proposed approach significantly improves over a baseline approach. |
How to Improve LLMs’ Performance on Specific Languages: A Perspective on LLM-Derived Language Similarity (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit uneven performance across languages. |
| Approach: | They propose to use a framework to quantify the similarity within each language pair through both the lenses of language-specific performance patterns and cross-lingual transferability. |
| Outcome: | The proposed approach outperforms traditional linguistic typology and cross-lingual transferability measures on multilingual LLMs. |
Measuring Cross-lingual Transfer in Bytes (2024.naacl-long)
Copied to clipboard
| Challenge: | Multilingual pretraining models can transfer knowledge to target languages with minimal or no examples . underlying mechanisms for this transfer remain unclear, with hypotheses ranging from language contamination to syntactic similarity. |
| Approach: | They conducted an experiment to investigate whether multilingual models transfer knowledge to target languages . they found that models initialized from diverse languages perform similarly to a target language . |
| Outcome: | a new study shows that models initialized from diverse languages perform similarly to a target language in a cross-lingual setting. |
Cross-Lingual Optimization for Language Transfer in Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Adapting large language models to other languages often suffers from an overemphasis on English performance. |
| Approach: | They propose a cross-lingual optimization technique that efficiently transfers an English-centric LLM to a target language while preserving its English capabilities. |
| Outcome: | The proposed model outperforms SFT in acquiring target language proficiency and maintaining English performance in low-resource languages. |
Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models (2024.eacl-long)
Copied to clipboard
| Challenge: | Recent advances in training multilingual models on large datasets have shown promising results in knowledge transfer across languages. |
| Approach: | They challenge the assumption that high zero-shot performance reflects high cross-lingual ability by introducing more challenging setups involving instances with multiple languages. |
| Outcome: | The proposed model can achieve high performance on multilingual benchmarks and on low-resource languages. |
Cross-lingual Transfer of Monolingual Models (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on cross-lingual learning using multilingual models cast doubt on shared vocabulary and joint pre-training . et al. (2005) show that model knowledge learned in the source language enhances the learning of the target language independently of language proximity. |
| Approach: | They propose a method for transferring monolingual models to other languages through continuous pre-training and investigate their results in English. |
| Outcome: | The proposed method outperforms a model trained from scratch in the GLUE benchmark for English . it shows that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in english. |
Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain factuality of model knowledge across languages, and preserve parity of language model performance. |
| Approach: | They examine pretrained and tuned models with code-mixed coreferential statements that convey identical knowledge across languages. |
| Outcome: | The proposed model shows different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and bottlenecks on a particular layer. |