A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models
Fuente:
arXiv
Guardado en:
| Autores principales: | Hayat, Ahatsham, Hasan, Mohammad Rashedul |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Leveraging Language Models for Analyzing Longitudinal Experiential Data in Education
por: Hayat, Ahatsham, et al.
Publicado: (2025)
por: Hayat, Ahatsham, et al.
Publicado: (2025)
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
por: Chang, Hongyan, et al.
Publicado: (2024)
por: Chang, Hongyan, et al.
Publicado: (2024)
Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models
por: Nguyen, Thao, et al.
Publicado: (2025)
por: Nguyen, Thao, et al.
Publicado: (2025)
Investigating Data Contamination for Pre-training Language Models
por: Jiang, Minhao, et al.
Publicado: (2024)
por: Jiang, Minhao, et al.
Publicado: (2024)
Model Merging in Pre-training of Large Language Models
por: Li, Yunshui, et al.
Publicado: (2025)
por: Li, Yunshui, et al.
Publicado: (2025)
Parallel Structures in Pre-training Data Yield In-Context Learning
por: Chen, Yanda, et al.
Publicado: (2024)
por: Chen, Yanda, et al.
Publicado: (2024)
DEPT: Decoupled Embeddings for Pre-training Language Models
por: Iacob, Alex, et al.
Publicado: (2024)
por: Iacob, Alex, et al.
Publicado: (2024)
MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models
por: Rakotonirina, Nathanaël Carraz, et al.
Publicado: (2024)
por: Rakotonirina, Nathanaël Carraz, et al.
Publicado: (2024)
Enhancing One-shot Pruned Pre-trained Language Models through Sparse-Dense-Sparse Mechanism
por: Li, Guanchen, et al.
Publicado: (2024)
por: Li, Guanchen, et al.
Publicado: (2024)
Making Pre-trained Language Models Great on Tabular Prediction
por: Yan, Jiahuan, et al.
Publicado: (2024)
por: Yan, Jiahuan, et al.
Publicado: (2024)
Exploiting Vocabulary Frequency Imbalance in Language Model Pre-training
por: Chung, Woojin, et al.
Publicado: (2025)
por: Chung, Woojin, et al.
Publicado: (2025)
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training
por: Liu, Lei, et al.
Publicado: (2025)
por: Liu, Lei, et al.
Publicado: (2025)
Core Context Aware Transformers for Long Context Language Modeling
por: Chen, Yaofo, et al.
Publicado: (2024)
por: Chen, Yaofo, et al.
Publicado: (2024)
MediSwift: Efficient Sparse Pre-trained Biomedical Language Models
por: Thangarasa, Vithursan, et al.
Publicado: (2024)
por: Thangarasa, Vithursan, et al.
Publicado: (2024)
PaPaformer: Language Model from Pre-trained Parallel Paths
por: Tapaninaho, Joonas, et al.
Publicado: (2025)
por: Tapaninaho, Joonas, et al.
Publicado: (2025)
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
por: Zheng, Junhao, et al.
Publicado: (2023)
por: Zheng, Junhao, et al.
Publicado: (2023)
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?
por: Soni, Nikita, et al.
Publicado: (2024)
por: Soni, Nikita, et al.
Publicado: (2024)
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
por: Samragh, Mohammad, et al.
Publicado: (2024)
por: Samragh, Mohammad, et al.
Publicado: (2024)
Fine-Tuning Pre-trained Language Models to Detect In-Game Trash Talks
por: Fesalbon, Daniel, et al.
Publicado: (2024)
por: Fesalbon, Daniel, et al.
Publicado: (2024)
Pre-trained Large Language Models Use Fourier Features to Compute Addition
por: Zhou, Tianyi, et al.
Publicado: (2024)
por: Zhou, Tianyi, et al.
Publicado: (2024)
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
por: Fan, Haozheng, et al.
Publicado: (2024)
por: Fan, Haozheng, et al.
Publicado: (2024)
Structural Pruning of Pre-trained Language Models via Neural Architecture Search
por: Klein, Aaron, et al.
Publicado: (2024)
por: Klein, Aaron, et al.
Publicado: (2024)
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models
por: Zhang, Ying, et al.
Publicado: (2024)
por: Zhang, Ying, et al.
Publicado: (2024)
Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training
por: Zhong, Zexuan, et al.
Publicado: (2024)
por: Zhong, Zexuan, et al.
Publicado: (2024)
Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings
por: Sharma, Kartik, et al.
Publicado: (2025)
por: Sharma, Kartik, et al.
Publicado: (2025)
Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens
por: Zhang, Anqi, et al.
Publicado: (2024)
por: Zhang, Anqi, et al.
Publicado: (2024)
ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data
por: Chen, Tong, et al.
Publicado: (2025)
por: Chen, Tong, et al.
Publicado: (2025)
Aligning Pre-trained Models for Spoken Language Translation
por: Sedláček, Šimon, et al.
Publicado: (2024)
por: Sedláček, Šimon, et al.
Publicado: (2024)
Sequence-to-Sequence Spanish Pre-trained Language Models
por: Araujo, Vladimir, et al.
Publicado: (2023)
por: Araujo, Vladimir, et al.
Publicado: (2023)
The Abstraction Gap in Vision-Language Causal Reasoning
por: Hoang, Chinh, et al.
Publicado: (2026)
por: Hoang, Chinh, et al.
Publicado: (2026)
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews
por: Boluki, Ali, et al.
Publicado: (2023)
por: Boluki, Ali, et al.
Publicado: (2023)
Efficient Continual Pre-training of LLMs for Low-resource Languages
por: Nag, Arijit, et al.
Publicado: (2024)
por: Nag, Arijit, et al.
Publicado: (2024)
The Dark Side of the Language: Pre-trained Transformers in the DarkNet
por: Ranaldi, Leonardo, et al.
Publicado: (2022)
por: Ranaldi, Leonardo, et al.
Publicado: (2022)
Integrating Pre-trained Language Model into Neural Machine Translation
por: Hwang, Soon-Jae, et al.
Publicado: (2023)
por: Hwang, Soon-Jae, et al.
Publicado: (2023)
CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models
por: Gu, Jiawei, et al.
Publicado: (2024)
por: Gu, Jiawei, et al.
Publicado: (2024)
Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models
por: Chen, Yuyan, et al.
Publicado: (2024)
por: Chen, Yuyan, et al.
Publicado: (2024)
Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers
por: Kadlčík, Marek, et al.
Publicado: (2025)
por: Kadlčík, Marek, et al.
Publicado: (2025)
Thinking Augmented Pre-training
por: Wang, Liang, et al.
Publicado: (2025)
por: Wang, Liang, et al.
Publicado: (2025)
Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models
por: Das, Anindya Sundar, et al.
Publicado: (2025)
por: Das, Anindya Sundar, et al.
Publicado: (2025)
A Context-Aware Dual-Metric Framework for Confidence Estimation in Large Language Models
por: Yuan, Mingruo, et al.
Publicado: (2025)
por: Yuan, Mingruo, et al.
Publicado: (2025)
Ejemplares similares
-
Leveraging Language Models for Analyzing Longitudinal Experiential Data in Education
por: Hayat, Ahatsham, et al.
Publicado: (2025) -
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
por: Chang, Hongyan, et al.
Publicado: (2024) -
Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models
por: Nguyen, Thao, et al.
Publicado: (2025) -
Investigating Data Contamination for Pre-training Language Models
por: Jiang, Minhao, et al.
Publicado: (2024) -
Model Merging in Pre-training of Large Language Models
por: Li, Yunshui, et al.
Publicado: (2025)