Cross-Lingual Transfer
291 papers with code • 1 benchmarks • 16 datasets
Cross-lingual transfer refers to transfer learning using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language.
Benchmarks
These leaderboards are used to track progress in Cross-Lingual Transfer
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Libraries
Use these libraries to find Cross-Lingual Transfer models and implementations
CoNLL
PAWS-X
TyDiQA
MLQA
CoNLL 2002
TyDiQA-GoldP
Tatoeba
Latest papers
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
In particular, most mainstream Code-LMs have been pre-trained on source code files alone.
Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language.
AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging
In this paper, we acknowledge the mutual reliance between task ability and language ability and direct our attention toward the gap between the target language and the source language on tasks.
Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages?
shaoyangxu/Multilingual-Human-Value-Concepts
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Drawing from our findings on multilingual value alignment, we prudently provide suggestions on the composition of multilingual data for LLMs pre-training: including a limited number of dominant languages for cross-lingual alignment transfer while avoiding their excessive prevalence, and keeping a balanced distribution of non-dominant languages.
Investigating Cultural Alignment of Large Language Models
The intricate relationship between language and culture has long been a subject of exploration within the realm of linguistic anthropology.
LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages.
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset
Different from previous works that simply translate English instructions, we consider both the language-specific and language-agnostic abilities of LLMs.
Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
fredxlpy/cross_lingual_prompt_tuning
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Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters.
Constrained Decoding for Cross-lingual Label Projection
duonglm38/codec
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Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e. g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data.
Translation Errors Significantly Impact Low-Resource Languages in Cross-Lingual Learning
csalt-research/translation-errors-crosslingual-learning
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Popular benchmarks (e. g., XNLI) used to evaluate cross-lingual language understanding consist of parallel versions of English evaluation sets in multiple target languages created with the help of professional translators.
