Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages (2021.tacl-1)
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| Challenge: | Currently, there are only 24 languages in the world that have not been annotated . transferring knowledge across domains is a common solution . |
| Approach: | They propose a Bayesian generative model for the space of neural parameters that factorizes into latent variables for each language and each task. |
| Outcome: | The proposed model can perform better than state-of-the-art methods with a typologically diverse sample of 33 languages from 4 continents and 11 families. |
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| Challenge: | a number of natural questions have been asked about the inductive biases of neural networks on core NLP tasks. |
| Approach: | They construct an informative prior for held-out languages on a task of character-level, open-vocabulary language modelling. |
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Multi Task Learning For Zero Shot Performance Prediction of Multilingual Models (2022.acl-long)
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| Challenge: | Massively Multilingual Transformer based Language Models have been shown to be effective on zero-shot transfer across languages, though performance varies from language to language depending on pivot language(s) used for fine-tuning. |
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Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
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| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
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Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
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Zero- and Few-Shot NLP with Pretrained Language Models (2022.acl-tutorials)
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| Challenge: | a tutorial aims to introduce NLP researchers to the latest techniques for learning from little-to-no data . aims at bringing interested researchers up to speed about the latest and ongoing techniques . |
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Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)
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Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
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Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
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| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
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Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
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Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
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Prompt Consistency for Zero-Shot Task Generalization (2022.findings-emnlp)
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| Challenge: | Recent work has shown that pre-trained language models can perform zero-shot generalization to new tasks without annotated examples. |
| Approach: | They propose to regularize prompt consistency to encourage consistent predictions over a diverse set of prompts. |
| Outcome: | The proposed approach outperforms the state-of-the-art zero-shot learner, T0, on 9 out of 11 datasets across 4 NLP tasks by 10.6 absolute points in terms of accuracy. |