Papers Few-Shot Text Classification
“Few-Shot Text Classification” 태그가 달린 논문 106편 · 필터 해제
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration
Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test instances, i.e., in-context learning. Howeve…
Few-Shot LearningFew-Shot Text ClassificationIn-Context LearningRetrieval+3Understanding BLOOM: An empirical study on diverse NLP tasks
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. W…
DecoderFew-Shot Text ClassificationQuestion Answeringtext-classification+3ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer Evaluation
Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Instead, machine evaluation can effectively …
Contrastive LearningData AugmentationFew-Shot LearningFew-Shot Text Classification+2Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering
Few-Shot Text Classification (FSTC) imitates humans to learn a new text classifier efficiently with only few examples, by leveraging prior knowledge from historical tasks. However, most prior works assume that all the ta…
ClusteringFew-Shot Text Classificationtext-classificationText ClassificationSTPrompt: Semantic-guided and Task-driven prompts for Effective Few-shot Classification
The effectiveness of prompt learning has been demonstrated in different pre-trained language models. By formulating suitable template and choosing representative label mapping, prompt learning can be used as an efficient…
Few-Shot LearningFew-Shot Text ClassificationPrompt Learningtext-classification+1Discriminative Language Model as Semantic Consistency Scorer for Prompt-based Few-Shot Text Classification
This paper proposes a novel prompt-based finetuning method (called DLM-SCS) for few-shot text classification by utilizing the discriminative language model ELECTRA that is pretrained to distinguish whether a token is ori…
Few-Shot Text ClassificationLanguage ModelingLanguage Modellingtext-classification+1Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP). However, fine tuning such models still requires a large number of training examples fo…
Few-Shot Text ClassificationMeta-Learningtext-classificationText ClassificationOCD: Learning to Overfit with Conditional Diffusion Models
We present a dynamic model in which the weights are conditioned on an input sample x and are learned to match those that would be obtained by finetuning a base model on x and its label y. This mapping between an input sa…
3D ReconstructionDenoisingFew-Shot Text Classificationimage-classification+2MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification
Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attenti…
Few-Shot LearningFew-Shot Text ClassificationMeta-Learningtext-classification+1Sentence-aware Adversarial Meta-Learning for Few-Shot Text Classification
Meta-learning has emerged as an effective approach for few-shot text classification. However, current studies fail to realize the importance of the semantic interaction between sentence features and neglect to enhance th…
ClassificationFew-Shot Text ClassificationMeta-LearningRepresentation Learning+3Visual Prompt Tuning for Few-Shot Text Classification
Deploying large-scale pre-trained models in the prompt-tuning paradigm has demonstrated promising performance in few-shot learning. Particularly, vision-language pre-training models (VL-PTMs) have been intensively explor…
ClassificationFew-Shot LearningFew-Shot Text Classificationimage-classification+4Few-shot Text Classification with Dual Contrastive Consistency
In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification where only a few annotated examples are given for each class. Since using traditional cross-entropy loss to fine…
ClassificationContrastive LearningFew-Shot Text ClassificationLanguage Modeling+4Efficient Few-Shot Learning Without Prompts
Recent few-shot methods, such as parameter-efficient fine-tuning (PEFT) and pattern exploiting training (PET), have achieved impressive results in label-scarce settings. However, they are difficult to employ since they a…
Few-Shot LearningFew-Shot Text Classificationparameter-efficient fine-tuningSentenceAdaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
Few-shot text classification aims to classify the text under the few-shot scenario. Most of the previous methods adopt optimization-based meta learning to obtain task distribution. However, due to the neglect of matching…
Few-Shot Text ClassificationMeta-Learningtext-classificationText ClassificationPCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficul…
Data AugmentationDialogue GenerationFew-Shot Text ClassificationParaphrase Generation+2LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification
Text classification has achieved great success with the prosperity of deep learning and pre-trained language models. However, we often encounter labeled data deficiency problems in real-world text-classification tasks. T…
ClassificationDocument EmbeddingFew-Shot LearningFew-Shot Text Classification+3Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems …
Few-Shot Text ClassificationMemorizationNamed Entity Recognition (NER)Prompt Engineering+4PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on suff…
Data AugmentationFew-Shot LearningFew-Shot Text ClassificationNatural Language Understanding+2Towards Unified Prompt Tuning for Few-shot Text Classification
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamiliar with prompt-style expressions during…
ClassificationFew-Shot LearningFew-Shot Text ClassificationLanguage Modeling+6Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incu…
Few-Shot Text ClassificationIn-Context Learningparameter-efficient fine-tuning