Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) require vast datasets for pretraining, making it difficult to train LLMs from scratch for lowresource languages. |
| Approach: | They propose to transform a language of the GLUE benchmark and then fine tune a pretrained model on that dataset. |
| Outcome: | The proposed models recover from syntactic-style shifts, but cannot recover from vocabulary misalignment and embedding matrix re-initialization, even with continued pretraining on 15 million tokens. |
Similar Papers
Investigating Transferability in Pretrained Language Models (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work on deep NLP models has centered on probing, a method that involves training classifiers for different tasks on model representations. |
| Approach: | They propose a method for determining the impact of each pretrained layer on transfer task performance by ablation. |
| Outcome: | The proposed method shows that pretraining models improve performance on downstream tasks . the results highlight the limitations of methods that operate on frozen models or single data samples. |
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. |
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. |
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
Copied to clipboard
| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
| Approach: | They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process. |
| Outcome: | The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty. |
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
| Approach: | They analyze checkpoints during multilingual pretraining to identify when models acquire in-language and cross-lingual abilities. |
| Outcome: | The proposed model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones. |
Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
| Approach: | They propose to use multilingual masked language modeling to train a model on concatenated text from multiple languages to find universal latent symmetries in embedding spaces. |
| Outcome: | The proposed models can be trained on concatenated text from multiple languages without shared vocabulary or domain similarity. |
Translation Artifacts in Cross-lingual Transfer Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing cross-lingual transfer learning techniques involve human and machine translations. |
| Approach: | They propose to use machine translation to translate test set or training set to introduce subtle artifacts that have a notable impact in existing cross-lingual models. |
| Outcome: | The proposed translation process reduces the lexical overlap between the premise and hypothesis by 4.3 and 2.8 points . the proposed translation-test and zero-shot approaches improve on previous work . |
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
Unknown Script: Impact of Script on Cross-Lingual Transfer (2024.naacl-srw)
Copied to clipboard
| Challenge: | Existing models for high-resource languages are not available for all languages, and the vast majority of the world's languages are excluded from these models. |
| Approach: | They propose to use pre-trained models to analyze the effect of the target language and its script on cross-lingual transfer. |
| Outcome: | The proposed model is based on six models pre-trained on NER and POS tasks in the original script and romanized version. |
Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models. |
| Approach: | They propose to use English pretraining corpora to analyze their language composition . they find that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |
| Outcome: | The proposed model is not truly monolingual when pretrained at scale, the authors show . they show that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |