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Showing 1–34 of 34 papers

TitleStatusHype
TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model EncodingsCode1
LEAP: Learnable Pruning for Transformer-based ModelsCode1
Linear Connectivity Reveals Generalization StrategiesCode1
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language UnderstandingCode1
Contrastive Representation Learning for Exemplar-Guided Paraphrase GenerationCode1
DAWSON: Data Augmentation using Weak Supervision On Natural Language—0
Feedforward Legendre Memory Unit—0
Few-shot Multimodal Multitask Multilingual Learning—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Unsupervised Paraphrasing with Pretrained Language Models—0
Applying Transfer Learning for Improving Domain-Specific Search Experience Using Query to Question Similarity—0
BERMo: What can BERT learn from ELMo?—0
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing—0
CKG: Dynamic Representation Based on Context and Knowledge Graph—0
Cross-Architecture Distillation Using Bidirectional CMOW Embeddings—0
On the Importance of Local Information in Transformer Based Models—0
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks—0
Privacy Adhering Machine Un-learning in NLP—0
Increasing Robustness to Spurious Correlations using Forgettable Examples—0
Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT—0
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching—0
Have Attention Heads in BERT Learned Constituency Grammar?—0
Increasing Robustness to Spurious Correlations using Forgettable Examples—0
KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding—0
Margin-Based Regularization and Selective Sampling in Deep Neural Networks—0
Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance LevelCode0
Enhancing Text Generation with Cooperative TrainingCode0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
BatchPrompt: Accomplish more with lessCode0
AGRO: Adversarial Discovery of Error-prone groups for Robust OptimizationCode0
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