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06/07/2023
Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
This paper reexamines the research on out-of-distribution (OOD) robustne...
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05/29/2023
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
Textual adversarial attacks can discover models' weaknesses by adding se...
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04/17/2023
Tool Learning with Foundation Models
Humans possess an extraordinary ability to create and utilize tools, all...
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12/16/2022
Decoder Tuning: Efficient Language Understanding as Decoding
With the evergrowing sizes of pre-trained models (PTMs), it has been an ...
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11/10/2022
Few-shot Classification with Hypersphere Modeling of Prototypes
Metric-based meta-learning is one of the de facto standards in few-shot ...
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10/31/2022
A Close Look into the Calibration of Pre-trained Language Models
Pre-trained language models (PLMs) achieve remarkable performance on man...
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10/19/2022
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
Textual adversarial samples play important roles in multiple subfields o...
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06/17/2022
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
Textual backdoor attacks are a kind of practical threat to NLP systems. ...
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04/11/2022
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm
Prompt-based learning paradigm bridges the gap between pre-training and ...
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03/18/2022
Prototypical Verbalizer for Prompt-based Few-shot Tuning
Prompt-based tuning for pre-trained language models (PLMs) has shown its...
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06/15/2021
Evaluating Modules in Graph Contrastive Learning
The recent emergence of contrastive learning approaches facilitates the ...
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07/03/2020
Adaptive Graph Encoder for Attributed Graph Embedding
Attributed graph embedding, which learns vector representations from gra...
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12/20/2018