Papers MRPC
“MRPC” 태그가 달린 논문 30편 · 필터 해제
Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks
Large Language Models (LLMs) have significantly advanced Natural Language Processing (NLP), particularly in Natural Language Understanding (NLU) tasks. As we progress toward an agentic world where LLM-based agents autono…
CoLAContinual LearningMRPCNatural Language Understanding+2Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic Similarity
This paper investigates the efficacy of RWKV, a novel language model architecture known for its linear attention mechanism, for generating sentence embeddings in a zero-shot setting. I conduct a layer-wise analysis to ev…
GPULanguage ModelingLanguage ModellingMRPC+6Generating 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+2Unlocking the Global Synergies in Low-Rank Adapters
Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the …
MRPCparameter-efficient fine-tuningA 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+4Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models
Fine-tuning large pre-trained language models for downstream tasks remains a critical challenge in natural language processing. This paper presents an empirical analysis comparing two efficient fine-tuning methods - BitF…
CoLAMRPCSTSSTS-BDACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining
Building on the cost-efficient pretraining advancements brought about by Crammed BERT, we enhance its performance and interpretability further by introducing a novel pretrained model Dependency Agreement Crammed BERT (DA…
GPUMRPCNatural Language UnderstandingRTEMerA: Merging Pretrained Adapters For Few-Shot Learning
Adapter tuning, which updates only a few parameters, has become a mainstream method for fine-tuning pretrained language models to downstream tasks. However, it often yields subpar results in few-shot learning. AdapterFus…
Few-Shot LearningMRPCGradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models
In this paper, we study the problem of generating obstinate (over-stability) adversarial examples by word substitution in NLP, where input text is meaningfully changed but the model's prediction does not, even though it …
MemorizationMRPCSST-2Abstract 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+11Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models
Through exploiting a high level of parallelism enabled by graphics processing units, transformer architectures have enabled tremendous strides forward in the field of natural language processing. In a traditional masked …
Language ModelingLanguage ModellingMRPCEnhancing 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 GenerationCKG: 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 GraphsMRPCQQPEnhancing Task-Specific Distillation in Small Data Regimes through Language Generation
Large-scale pretrained language models have led to significant improvements in Natural Language Processing. Unfortunately, they come at the cost of high computational and storage requirements that complicate their deploy…
Data AugmentationMRPCSST-2Text GenerationAn Automatic and Efficient BERT Pruning for Edge AI Systems
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained devices for low-latency and high accuracy.…
CPUModel CompressionMRPCQNLI+3Towards Better Characterization of Paraphrases
To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alt…
Data AugmentationMRPCParaphrase GenerationParaphrase Identification+1LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory
LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example. To improve the approach of LM-BFF, this paper proposes LM-BFF-MS—better f…
MRPCSST-2Word EmbeddingsDRONE: Data-aware Low-rank Compression for Large NLP Models
The representations learned by large-scale NLP models such as BERT have been widely used in various tasks. However, the increasing model size of the pre-trained models also brings efficiency challenges, including inferen…
Low-rank compressionMRPCNatural Language InferenceEfficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity
Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task. A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, wh…
Auxiliary LearningMRPCMulti-Task LearningRTE+2Towards Better Characterization of Paraphrases
To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alt…
Data AugmentationMRPCParaphrase GenerationParaphrase Identification+1