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20162024
most citedHow Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

36 citations · 67 across the 12 of their papers we have counts for

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Showing cs.CLShow all

5 papers · 1 filter

cs.CL2023

Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild

Ting Wu, Jingyi Liu, Rui Zheng +3

The principle of continual relation extraction~(CRE) involves adapting to emerging novel relations while preserving od knowledge. While current endeavors in CRE succeed in preservi…

cs.CL2023

CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing

Songyang Gao, Shihan Dou, Junjie Shan +2

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…

cs.CL202336 cited

How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

Xuanting Chen, Junjie Ye, Can Zu +7

The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. Ho…

cs.CL20229 cited

Causal Intervention Improves Implicit Sentiment Analysis

Siyin Wang, Jie Zhou, Changzhi Sun +4

Despite having achieved great success for sentiment analysis, existing neural models struggle with implicit sentiment analysis. This may be due to the fact that they may latch onto…

cs.CL20162 cited

Learning Word Embeddings from Intrinsic and Extrinsic Views

Jifan Chen, Kan Chen, Xipeng Qiu +3

While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word em…