Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation

By Tu Vu, Aditya Barua, Brian Lester, Daniel Matthew Cer, Mohit Iyyer, Noah Constant. 2022

75 citations recorded by the source corpus.

In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study. We assume a strict setting with no access to parallel data or machine translation and find that common transfer learning approaches struggle in this setting, as a generative multilingual model fine-tuned purely on English catastrophically forgets how to generate non-English. Given the recent rise of parameter-efficient adaptation techniques, we conduct the first investigation into how one such method, prompt tuning (Lester et al., 2021), can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. Our experiments show that parameter-efficient prompt tuning provides gains over standard fine-tuning when transferring between less-related languages, e.g., from English to Thai. However, a significant gap still remains between these methods and fully-supervised baselines. To improve cross-lingual transfer further, we explore several approaches, including: (1) mixing in unlabeled multilingual data, and (2) explicitly factoring prompts into recombinable language and task components. Our approaches can provide further quality gains, suggesting that robust zero-shot cross-lingual generation is within reach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Natural Language Processing

In Representation & Self-Supervised Learning

Citations in the corpus

Pretraining Language Models with Human Preferences (2023, background): “, 2022), but this strategy is also limited by the fact that large LMs are quite resistant to forgetting their training data (an effect that increases with model size; Carlini et al., 2022; Vu et al., 2022; Ramasesh et al., 2022).”
LoRA Learns Less and Forgets Less (2024, methodology): “This work sheds light on the downstream performance of 7 billion parameter LLMs trained with LoRA and full finetuning.”