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A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities
Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …
Efficient utilization of pre-trained models: A review of sentiment analysis via prompt learning
K Bu, Y Liu, X Ju - Knowledge-Based Systems, 2024 - Elsevier
Sentiment analysis is one of the traditional well-known tasks in Natural Language
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …
Processing (NLP) research. In recent years, Pre-trained Models (PMs) have become one of …
Fill in the blank: Context-aware automated text input generation for mobile gui testing
Automated GUI testing is widely used to help ensure the quality of mobile apps. However,
many GUIs require appropriate text inputs to proceed to the next page, which remains a …
many GUIs require appropriate text inputs to proceed to the next page, which remains a …
PLACES: Prompting language models for social conversation synthesis
M Chen, A Papangelis, C Tao, S Kim… - ar** plms sauce: Bridging structure and text for effective knowledge graph completion via conditional soft prompting
Knowledge Graph Completion (KGC) often requires both KG structural and textual
information to be effective. Pre-trained Language Models (PLMs) have been used to learn …
information to be effective. Pre-trained Language Models (PLMs) have been used to learn …
Language-guided music recommendation for video via prompt analogies
We propose a method to recommend music for an input video while allowing a user to guide
music selection with free-form natural language. A key challenge of this problem setting is …
music selection with free-form natural language. A key challenge of this problem setting is …
Let gpt be a math tutor: Teaching math word problem solvers with customized exercise generation
In this paper, we present a novel approach for distilling math word problem solving
capabilities from large language models (LLMs) into smaller, more efficient student models …
capabilities from large language models (LLMs) into smaller, more efficient student models …
Label-specific feature augmentation for long-tailed multi-label text classification
Multi-label text classification (MLTC) involves tagging a document with its most relevant
subset of labels from a label set. In real applications, labels usually follow a long-tailed …
subset of labels from a label set. In real applications, labels usually follow a long-tailed …
Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning
Motivation Few-shot learning that can effectively perform named entity recognition in low-
resource scenarios has raised growing attention, but it has not been widely studied yet in the …
resource scenarios has raised growing attention, but it has not been widely studied yet in the …