Reordering Examples Helps during Priming-based Few-Shot Learning (2021.findings-acl)
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| Challenge: | Existing methods for learning from limited data are not efficient . we show that presenting examples in the right order is key for generalization . |
| Approach: | They propose a method to learn from limited data using examples as prompts . they propose PERO, which uses examples as search over set of permutations . |
| Outcome: | The proposed method can generalize using as few as 10 examples, the authors show . it can be used on sentiment classification, natural language inference and fact retrieval tasks . |
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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. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |
Designing Informative Metrics for Few-Shot Example Selection (2024.findings-acl)
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| Challenge: | Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. |
| Approach: | They propose a complexity-based prompt selection approach for sequence tagging tasks that uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. |
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Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022.acl-long)
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| Challenge: | Large pretrained language models can generate text classification results that match fully supervised models. |
| Approach: | They propose to use a few sample training to determine which permutations are performant . they use generative language models to construct an artificial development set . |
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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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Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning (2024.naacl-srw)
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| Challenge: | Parameter-efficient (PE) methods for adapting pre-trained language models to downstream tasks are still lacking in many cases. |
| Approach: | They propose a general PE priming framework to enhance few-shot adaptation and generalization ability of PE methods. |
| Outcome: | The proposed framework reveals that the best priming strategy facilitates adaptation to target tasks. |
Few-Shot Self-Rationalization with Natural Language Prompts (2022.findings-naacl)
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| Challenge: | Existing models that generate free-text explanations for tasks are limited by human-written explanations. |
| Approach: | They propose to use a standardized collection of natural language prompts to create a model that generates free-text explanations for tasks. |
| Outcome: | The proposed model can predict task labels and generate free-text explanations for predictions . plausibility of human explanations is 76%, while human explanation is 51% . |
Instance-Guided Prompt Learning for Few-Shot Text Matching (2022.findings-emnlp)
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| Challenge: | Few-shot text matching is a more practical technique to determine whether two texts are semantically identical. |
| Approach: | They propose a pluggable prompt learning method for few-shot text matching . they use the semantics of instances to regulate the effects of the gate on the prompt tokens . |
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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 . |
| Approach: | They aim to introduce techniques for learning from little-to-no data using pretrained language models. |
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Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models (2022.emnlp-main)
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| Challenge: | Pre-trained masked language models perform few-shot learning, but discriminative models like ELECTRA do not fit into the paradigm. |
| Approach: | They propose to use ELECTRA to train pre-trained models to score originality of target options without introducing new parameters. |
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BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions (2023.findings-emnlp)
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| Challenge: | Existing approaches to text classification require large annotated corpora to train or long context to fit many examples. |
| Approach: | They propose a method to few-shot text classification using an LLM. |
| Outcome: | The proposed approach yields high accuracy classifiers within 79% of the performance of models trained with larger datasets while using only 1% of their training sets. |