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Meizhen Liu

2 accepted papers

2023

InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer

EMNLP 2023long findings

Multilingual language models (MLLMs) have achieved remarkable success in various cross-lingual transfer tasks. However, they suffer poor performance in zero-shot low-resource languages, particularly when dealing with longer contexts. Existing research mainly relies on full-model fine-tuning on large…

Cited by 0SourceScholar
2022

Adapters for Enhanced Modeling of Multilingual Knowledge and Text

EMNLP 2022finding

Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual langua…