Papers WNLI
“WNLI” 태그가 달린 논문 8편 · 필터 해제
Time Travel in LLMs: Tracing Data Contamination in Large Language Models
Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on other tasks. We propose …
In-Context LearningWNLISmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference
Dynamic early exiting has been proven to improve the inference speed of the pre-trained language model like BERT. However, all samples must go through all consecutive layers before early exiting and more complex samples …
Contrastive LearningLanguage ModellingRTEWNLIBag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tas…
Contrastive LearningDenoisingLanguage ModelingLanguage Modelling+11Understanding BLOOM: An empirical study on diverse NLP tasks
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. W…
DecoderFew-Shot Text ClassificationQuestion Answeringtext-classification+3WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
Pronoun resolution is a major area of natural language understanding. However, large-scale training sets are still scarce, since manually labelling data is costly. In this work, we introduce WikiCREM (Wikipedia CoREferen…
coreference-resolutionCoreference ResolutionLanguage ModelingLanguage Modelling+2A Hybrid Neural Network Model for Commonsense Reasoning
This paper proposes a hybrid neural network (HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERT-based contextual enc…
Common Sense ReasoningCoreference ResolutionLanguage ModelingLanguage Modelling+6A Surprisingly Robust Trick for the Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of th…
Language ModelingLanguage ModellingNatural Language UnderstandingWNLIA Surprisingly Robust Trick for Winograd Schema Challenge
The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of th…
Common Sense ReasoningCoreference ResolutionLanguage ModelingLanguage Modelling+3