Few-Shot Text Classification
42 papers with code • 8 benchmarks • 4 datasets
Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances 1
Benchmarks
These leaderboards are used to track progress in Few-Shot Text Classification
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Libraries
Use these libraries to find Few-Shot Text Classification models and implementations
SST
SST-5
RAFT
Events classification - Biotech news
Latest papers with no code
Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning
In addressing this issue, we are inspired by the notion that a backdoor acts as a shortcut and posit that this shortcut stems from the contrast between the trigger and the data utilized for poisoning.
CrossTune: Black-Box Few-Shot Classification with Label Enhancement
Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks.
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.
A 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.
Label-Aware Automatic Verbalizer for Few-Shot Text Classification
Specifically, we use the manual labels along with the conjunction "and" to induce the model to generate more effective words for the verbalizer.
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
As a result, it is not possible for end-users to build classifiers for themselves.
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks.
Breaking 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.
Emotion-Conditioned Text Generation through Automatic Prompt Optimization
We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure.
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data.
