Papers Few-Shot Text Classification
“Few-Shot Text Classification” 태그가 달린 논문 106편 · 필터 해제
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
Pre-trained Language Models (PLMs) can be accurately fine-tuned for downstream text processing tasks. Recently, researchers have introduced several parameter-efficient fine-tuning methods that optimize input prompts or a…
Few-Shot Text Classificationparameter-efficient fine-tuningtext-classificationText ClassificationA Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields. Different from general text classification (e.g., topic classification), few-shot se…
ClassificationContrastive LearningFew-Shot Text ClassificationSentiment Analysis+4Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification
Recently, prompt-based fine-tuning has garnered considerable interest as a core technique for few-shot text classification task. This approach reformulates the fine-tuning objective to align with the Masked Language Mode…
ClassificationFew-Shot Text ClassificationLanguage ModelingLanguage Modelling+4Label-Aware Automatic Verbalizer for Few-Shot Text Classification
Prompt-based learning has shown its effectiveness in few-shot text classification. One important factor in its success is a verbalizer, which translates output from a language model into a predicted class. Notably, the s…
Few-Shot Text ClassificationLanguage ModelingLanguage Modellingtext-classification+1BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
Text classification is a well-studied and versatile building block for many NLP applications. Yet, existing approaches require either large annotated corpora to train a model with or, when using large language models as …
Few-Shot Text Classificationtext-classificationText ClassificationTransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
Text classification is one of the most imperative tasks in natural language processing (NLP). Recent advances with pre-trained language models (PLMs) have shown remarkable success on this task. However, the satisfying re…
Few-Shot LearningFew-Shot Text Classificationtext-classificationText ClassificationBreaking the Bank with ChatGPT: Few-Shot Text Classification for Finance
We propose the use of conversational GPT models for easy and quick few-shot text classification in the financial domain using the Banking77 dataset. Our approach involves in-context learning with GPT-3.5 and GPT-4, which…
Contrastive LearningFew-Shot Text ClassificationGPUIn-Context Learning+2Emotion-Conditioned Text Generation through Automatic Prompt Optimization
Conditional natural language generation methods often require either expensive fine-tuning or training a large language model from scratch. Both are unlikely to lead to good results without a substantial amount of data a…
Conditional Text GenerationFew-Shot Text ClassificationLanguage ModelingLanguage Modelling+6Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification
Recent advances for few-shot text classification aim to wrap textual inputs with task-specific prompts to cloze questions. By processing them with a masked language model to predict the masked tokens and using a verbaliz…
Few-Shot Text ClassificationLanguage ModelingLanguage Modellingtext-classification+1MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification
Prompting methods have shown impressive performance in a variety of text mining tasks and applications, especially few-shot ones. Despite the promising prospects, the performance of prompting model largely depends on the…
ClassificationFew-Shot Text Classificationtext-classificationText ClassificationTART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class varian…
Few-Shot Text ClassificationMeta-Learningtext-classificationText ClassificationEnhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical a…
Data AugmentationFew-Shot Text ClassificationLanguage ModelingLanguage Modelling+2Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effective on various text classification tasks…
ClassificationData AugmentationFew-Shot Text Classificationtext-classification+1ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification
Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite …
ClassificationContrastive LearningFew-Shot Text ClassificationMeta-Learning+2Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
The prompt-based learning paradigm, which bridges the gap between pre-training and fine-tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot settings. Despite being widely applied,…
Backdoor AttackFew-Shot Text Classificationtext-classificationText ClassificationBoosting Few-Shot Text Classification via Distribution Estimation
Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred acro…
ClassificationFew-Shot Image ClassificationFew-Shot Text Classificationimage-classification+3MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on train…
Few-Shot Text ClassificationMeta-LearningMisinformationtext-classification+1Mask-guided BERT for Few Shot Text Classification
Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource …
ClassificationContrastive LearningFew-Shot LearningFew-Shot Text Classification+3Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification
Few-shot text classification systems have impressive capabilities but are infeasible to deploy and use reliably due to their dependence on prompting and billion-parameter language models. SetFit (Tunstall et al., 2022) i…
ClassificationContrastive LearningFew-Shot Text ClassificationSentence+2Meta-Learning Siamese Network for Few-Shot Text Classification
Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the succ…
ClassificationDescriptiveFew-Shot LearningFew-Shot Text Classification+3