Papers Text Augmentation
“Text Augmentation” 태그가 달린 논문 97편 · 필터 해제
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task's objective is to evaluate explainable classification systems for classifying hazards and product…
Data AugmentationText AugmentationBatch Aggregation: An Approach to Enhance Text Classification with Correlated Augmented Data
Natural language processing models often face challenges due to limited labeled data, especially in domain specific areas, e.g., clinical trials. To overcome this, text augmentation techniques are commonly used to increa…
ClassificationText Augmentationtext-classificationText ClassificationToward General and Robust LLM-enhanced Text-attributed Graph Learning
Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LLM-enhanced TAG learning as a critical research area. By utilizing rich g…
Graph LearningTAGText AugmentationWords or Vision: Do Vision-Language Models Have Blind Faith in Text?
Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality prefere…
Language ModelingLanguage ModellingText AugmentationLaser: Efficient Language-Guided Segmentation in Neural Radiance Fields
In this work, we propose a method that leverages CLIP feature distillation, achieving efficient 3D segmentation through language guidance. Unlike previous methods that rely on multi-scale CLIP features and are limited by…
SegmentationText AugmentationImage, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A Survey
In the past five years, research has shifted from traditional Machine Learning (ML) and Deep Learning (DL) approaches to leveraging Large Language Models (LLMs) , including multimodality, for data augmentation to enhance…
Data AugmentationImage AugmentationText AugmentationMultimodal AI on Wound Images and Clinical Notes for Home Patient Referral
Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, making regular care essential to ensure healing and prevent severe outcom…
Text AugmentationTransfer LearningTARDiS : Text Augmentation for Refining Diversity and Separability
Text augmentation (TA) is a critical technique for text classification, especially in few-shot settings. This paper introduces a novel LLM-based TA method, TARDiS, to address challenges inherent in the generation and ali…
DiversityFew-Shot Text ClassificationText Augmentationtext-classification+1Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP
Zero-shot action recognition (ZSAR) requires collaborative multi-modal spatiotemporal understanding. However, finetuning CLIP directly for ZSAR yields suboptimal performance, given its inherent constraints in capturing e…
Action RecognitionText AugmentationZero-Shot Action RecognitionAn Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains
Named Entity Recognition (NER) is a machine learning task that traditionally relies on supervised learning and annotated data. Acquiring such data is often a challenge, particularly in specialized fields like medical, le…
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification
The generative large language models (LLMs) are increasingly used for data augmentation tasks, where text samples are paraphrased (or generated anew) and then used for classifier fine-tuning. Existing works on augmentati…
Data AugmentationFew-Shot LearningText AugmentationCleanerCLIP: Fine-grained Counterfactual Semantic Augmentation for Backdoor Defense in Contrastive Learning
Pre-trained large models for multimodal contrastive learning, such as CLIP, have been widely recognized in the industry as highly susceptible to data-poisoned backdoor attacks. This poses significant risks to downstream …
backdoor defenseContrastive LearningcounterfactualText Augmentation+2Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning
Audio-text contrastive models have become a powerful approach in music representation learning. Despite their empirical success, however, little is known about the influence of key design choices on the quality of music-…
DiversityRepresentation LearningText AugmentationLLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?
The generative large language models (LLMs) are increasingly being used for data augmentation tasks, where text samples are LLM-paraphrased and then used for classifier fine-tuning. However, a research that would confirm…
Data AugmentationText AugmentationQAEA-DR: A Unified Text Augmentation Framework for Dense Retrieval
In dense retrieval, embedding long texts into dense vectors can result in information loss, leading to inaccurate query-text matching. Additionally, low-quality texts with excessive noise or sparse key information are un…
Answer GenerationEvent ExtractionQuestion-Answer-GenerationRetrieval+5Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?
Background: Software Vulnerability (SV) assessment is increasingly adopted to address the ever-increasing volume and complexity of SVs. Data-driven approaches have been widely used to automate SV assessment tasks, partic…
Data AugmentationText AugmentationPerformance Improvement of Language-Queried Audio Source Separation Based on Caption Augmentation From Large Language Models for DCASE Challenge 2024 Task 9
We present a prompt-engineering-based text-augmentation approach applied to a language-queried audio source separation (LASS) task. To enhance the performance of LASS, the proposed approach utilizes large language models…
Audio Source SeparationPrompt EngineeringSentenceText AugmentationAn efficient text augmentation approach for contextualized Mandarin speech recognition
Although contextualized automatic speech recognition (ASR) systems are commonly used to improve the recognition of uncommon words, their effectiveness is hindered by the inherent limitations of speech-text data availabil…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition+1ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis
SMS, or short messaging service, is a widely used and cost-effective communication medium that has sadly turned into a haven for unwanted messages, commonly known as SMS spam. With the rapid adoption of smartphones and I…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Language ModelingLanguage Modelling+2Context-Aware Clustering using Large Language Models
Despite the remarkable success of Large Language Models (LLMs) in text understanding and generation, their potential for text clustering tasks remains underexplored. We observed that powerful closed-source LLMs provide g…
ClusteringLanguage ModelingLanguage ModellingText Augmentation+2