Semanlink - Few-shot learning ; Unsupervised machine learning AND Arxiv Doc

[2104.11882] Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

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2022-10-25 About

[2209.11055] Efficient Few-Shot Learning Without Prompts

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2022-09-23 About

[2209.01975] Selective Annotation Makes Language Models Better Few-Shot Learners

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2022-09-07 About

[2208.03299] Few-shot Learning with Retrieval Augmented Language Model

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2022-08-08 About

[2203.14655] Few-Shot Learning with Siamese Networks and Label Tuning

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> the problem of building text classifiers with little or no training data.
>
> In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks.

(cf. #[NLI](tag:nli), using the input text as the premise and the text representing the label as the hypothesis)

> In this work, we show that **with proper pre-training, Siamese Networks that embed texts and labels** offer a competitive alternative.
>
> We introduce **label tuning: fine-tuning the label embeddings only**. While giving lower performance than model fine-tuning (which updates all params of the model), this approach has the architectural advantage that a single encoder can be shared by many different tasks (we only fine-tune the label embeddings)
> The drop in quality can
be compensated by using a variant of **[Knowledge distillation](tag:knowledge_distillation)**

[Github](https://tinyurl.com/label-tuning), [Tweet](doc:2022/03/thomas_muller_sur_twitter_pa)

2022-03-30 About

[1712.05972] Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

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2021-10-16 About

[2010.07245] Text Classification Using Label Names Only: A Language Model Self-Training Approach

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2021-10-16 About

[2012.15723] Making Pre-trained Language Models Better Few-shot Learners

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2021-01-02 About