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Showing 1–25 of 30 papers

TitleStatusHype
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence RepresentationsCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification ContextCode1
Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks—0
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic SimilarityCode0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Unlocking the Global Synergies in Low-Rank Adapters—0
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching—0
Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models—0
DACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining—0
MerA: Merging Pretrained Adapters For Few-Shot Learning—0
Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models—0
Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models—0
Enhancing Text Generation with Cooperative TrainingCode0
CKG: Dynamic Representation Based on Context and Knowledge Graph—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration MemoryCode0
Towards Better Characterization of ParaphrasesCode0
DRONE: Data-aware Low-rank Compression for Large NLP Models—0
Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature SimilarityCode0
Towards Better Characterization of ParaphrasesCode0
Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task—0
Data-aware Low-Rank Compression for Large NLP Models—0
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