Papers with Few-shot classification
MEAL: Stable and Active Learning for Few-Shot Prompting (2023.findings-emnlp)
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| Challenge: | Existing methods for few-shot classification have high variance across different sets of few shots and finetuning runs. |
| Approach: | They propose novel ensembling methods that significantly reduce run variability and introduce a new active learning criterion for *data selection*. |
| Outcome: | The proposed method significantly reduces run variability and improves performance on five tasks. |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
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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. |
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models (2023.emnlp-main)
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Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Myriam Tami, Victor Storchan, Ismail Ayed, Pablo Piantanida
| Challenge: | Proprietary and closed APIs are impacting the practical applications of natural language processing. |
| Approach: | They propose a scenario where a pre-trained model is served through a gated API . they propose 'transductive inference' that leverages statistics of unlabelled data . |
| Outcome: | The proposed model performs a few-shot classification task with unlabelled data using a gated API . the proposed model can be used to perform the task with a handful of classes . |
Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification (2023.emnlp-main)
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| Challenge: | Existing methods for fewshot text classification require substantial computing power and data. |
| Approach: | They propose an efficient method to add task and label information to a sentence transformer model by contrastive learning and a triplet loss to enforce training instances to be closest to their own textual semantic label information. |
| Outcome: | The proposed method achieves strong performance in data-sparse scenarios compared to existing methods across SST-5, Emotion detection, and AG News data even with just two examples per class. |