| Challenge: | Existing methods for cold-start learning and recommendation are brittle to scenarios with few interactions. |
| Approach: | They propose a Few-shot learning method for Cold-Start recommendation that consists of three hierarchical structures that are local and global . |
| Outcome: | The proposed method improves on two public real-world datasets and is stable compared with the state-of-the-art. |
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| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
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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Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to learn text labels require large amounts of data to build many few-shot tasks. |
| Approach: | They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method. |
| Outcome: | The proposed method improves on four text classification datasets with high accuracy and robustness. |
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization (2023.findings-emnlp)
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| Challenge: | Existing methods for prompt tuning can overfit to few-shot training samples, causing overfitting . authors propose a new framework for prompt learning with supervised meta-learning . |
| Approach: | They propose a self-supervised meta-prompt learning framework with MEta-gradient Regularization for few-shot generalization that leverages self-recognized meta-learning with a diverse set of meta-tasks to learn a universal prompt initialization using only unlabeled data. |
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Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks (2024.findings-emnlp)
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| Challenge: | Existing methods for few-shot text classification often encounter problems drawing accurate class prototypes from support set samples. |
| Approach: | They propose a meta-learning method that leverages the information within the task itself . they propose Query-Data-Augmenter and Label-Adapter to build a task-adaptive metric space . |
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Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
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Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
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Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (2021.tacl-1)
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| Challenge: | a recent study has focused on few-shot learning (FSL) for relation classification, but it requires large amounts of training data. |
| Approach: | They propose a method for deriving more realistic few-shot test data from available datasets for supervised RC. |
| Outcome: | The proposed method yields a challenging benchmark for FSL-RC on which state of the art models show poor performance. |
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)
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| Challenge: | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. |
| Approach: | They propose to use few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. |
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Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)
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| Challenge: | Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances. |
| Approach: | They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types. |
| Outcome: | The proposed models outperform human models on relation extraction tasks while relying on entity type information. |