Papers QQP
“QQP” 태그가 달린 논문 34편 · 필터 해제
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tas…
Computational EfficiencyLanguage ModelingLanguage ModellingLarge Language Model+3Generating Synthetic Datasets for Few-shot Prompt Tuning
A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, limiting its scope of application. In this…
Few-Shot LearningMRPCQQPSentence+2A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
Sentence semantic matching is a research hotspot in natural language processing, which is considerably significant in various key scenarios, such as community question answering, searching, chatbot, and recommendation. S…
ChatbotCommunity Question AnsweringHow to refund a wrong transaction in PhonePeMRPC+4TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings
This paper presents the Text Encoding Diffusion Model (TEncDM), a novel approach to diffusion modeling that operates in the space of pre-trained language model encodings. In contrast to traditionally used embeddings, enc…
Conditional Text GenerationDecoderDenoisingLanguage Modeling+4BatchPrompt: Accomplish more with less
As the ever-increasing token limits of large language models (LLMs) have enabled long context as input, prompting with single data samples might no longer an efficient way. A straightforward strategy improving efficiency…
8kLanguage ModellingNatural Language InferenceQQP+2Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT
Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question answering, requiring only few epochs of fin…
QNLIQQPQuestion AnsweringSensitivity+3Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering …
Abstract Meaning RepresentationContrastive LearningData AugmentationLanguage Modelling+11CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing
Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models. Existing works focus on eliminating da…
Causal InferenceDisentanglementQQPvalidEnhancing Text Generation with Cooperative Training
Recently, there has been a surge in the use of generated data to enhance the performance of downstream models, largely due to the advancements in pre-trained language models. However, most prevailing methods trained gene…
MRPCQQPSTSText GenerationFew-shot Multimodal Multitask Multilingual Learning
While few-shot learning as a transfer learning paradigm has gained significant traction for scenarios with limited data, it has primarily been explored in the context of building unimodal and unilingual models. Furthermo…
Few-Shot LearningIn-Context LearningNatural Language UnderstandingNER+9Privacy Adhering Machine Un-learning in NLP
Regulations introduced by General Data Protection Regulation (GDPR) in the EU or California Consumer Privacy Act (CCPA) in the US have included provisions on the \textit{right to be forgotten} that mandates industry appl…
Machine UnlearningQQPCKG: Dynamic Representation Based on Context and Knowledge Graph
Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performance. As a result, they have achieved sta…
Knowledge GraphsMRPCQQPAGRO: Adversarial Discovery of Error-prone groups for Robust Optimization
Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distrib…
QQPLinear Connectivity Reveals Generalization Strategies
It is widely accepted in the mode connectivity literature that when two neural networks are trained similarly on the same data, they are connected by a path through parameter space over which test set accuracy is maintai…
CoLADiagnosticQQPTransfer LearningFrom Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression
Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quantities of parameters, PLMs are computation…
Contrastive LearningLanguage ModelingLanguage ModellingModel Compression+1DAWSON: Data Augmentation using Weak Supervision On Natural Language
We propose a novel data augmentation model for text, using all available data through weak supervision. To improve generalization, recent work in the field uses BERT and masked language modeling to conditionally augment …
Data AugmentationLanguage ModelingLanguage ModellingMasked Language Modeling+4BERMo: What can BERT learn from ELMo?
We propose BERMo, an architectural modification to BERT, which makes predictions based on a hierarchy of surface, syntactic and semantic language features. We use linear combination scheme proposed in Embeddings from Lan…
QQPSST-2Cross-Architecture Distillation Using Bidirectional CMOW Embeddings
Large pretrained language models (PreLMs) are revolutionizing natural language processing across all benchmarks. However, their sheer size is prohibitive for small laboratories or deployment on mobile devices. Approaches…
Linguistic AcceptabilityQQPRTESentenceContrastive Representation Learning for Exemplar-Guided Paraphrase Generation
Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. In this paper, we prop…
Contrastive LearningDecoderParaphrase GenerationPOS+3LEAP: Learnable Pruning for Transformer-based Models
Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current pruning algorithms either only focus on one pruning category…
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