One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging for Cross-Lingual Transfer (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for zero-shot cross-lingual transfer are unreliable due to the lack of pretraining data. |
| Approach: | They propose to accumulatively average model snapshots from different runs into a single model. |
| Outcome: | The proposed protocol decouples performance maximization from hyperparameter tuning. |
Similar Papers
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging (2023.acl-long)
Copied to clipboard
| Challenge: | Massively multilingual language models have shown strong performance in zero-shot (ZS-XLT) and few-shot cross-lingual transfer setups where models are fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). |
| Approach: | They propose a method that averages different checkpoints during task fine-tuning to improve model robustness. |
| Outcome: | The proposed method overestimates model performance in cross-lingual transfer setups where models are evaluated at checkpoints that generalize best to validation instances in the target languages. |
Don’t Stop Fine-Tuning: On Training Regimes for Few-Shot Cross-Lingual Transfer with Multilingual Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work highlights the fallacies of zero-shot cross-lingual transfer with large multilingual models. |
| Approach: | They propose to replace sequential fine-tuning with joint fine-uning on source and target language instances. |
| Outcome: | The proposed techniques yield improved and more stable FS-XLT across the board. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
Massively Multilingual Transfer for NER (P19-1)
Copied to clipboard
| Challenge: | Existing approaches for cross-lingual transfer use a single source language, but there are exceptions. |
| Approach: | They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. |
| Outcome: | The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model. |
Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations (2024.naacl-long)
Copied to clipboard
| Challenge: | Zero-Shot Cross-lingual transfer (ZS-XLT) uses a model trained in a source language to make predictions in another language, often with a performance loss. |
| Approach: | They propose a new approach that uses In-Context Tuning to train a model to learn from context examples and adapt it to a target language by prepending a One-Shot context demonstration. |
| Outcome: | The proposed approach outperforms prompt-based models in Zero-Shot and Few-shot scenarios with target-language examples. |
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing multilingual models cannot fully leverage training data when it is available in different task-language combinations. |
| Approach: | They propose a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation. |
| Outcome: | The proposed model achieves the best or competitive gain when a mixture of multiple resources is available while being significantly more efficient than existing models. |
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. |
Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained multilingual language models show significant performance gains for zero-shot cross-lingual model transfer on a wide range of natural language understanding (NLU) tasks. |
| Approach: | They do cross-lingual evaluation using prompt tuning and compare it with fine-tuning . prompt tuning achieves much better cross-linguistic transfer than fine- tuning . |
| Outcome: | The results show that prompt tuning achieves better cross-lingual transfer than fine-tuning across datasets, with only 0.1% to 0.3% tuned parameters. |
X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance when evaluated in non-English languages. |
| Approach: | They propose to use sub-network similarity between two languages as a proxy for XLT prediction. |
| Outcome: | The proposed method shows proficiency in ranking candidates for zero-shot XLT, achieving an improvement of 4.6% on average in terms of NDCG@3. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |