TI-Prompt: Towards a Prompt Tuning Method for Few-shot Threat Intelligence Twitter Classification-Reference-Cited by-同舟云学术

TI-Prompt: Towards a Prompt Tuning Method for Few-shot Threat Intelligence Twitter Classification

Published:2022-06 Issue: Volume: Page:
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Container-title:2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)
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Author:

You Yizhe¹,Jiang Zhengwei¹,Zhang Kai¹,Jiang Jun¹,Wang Xuren²,Zhang Zheyu³,Wang Shirui³,Feng Huamin⁴

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences,Beijing,China

2. Information Engineering College, Capital Normal University,Beijing,China

3. China Industrial Control Systems Cyber Emergency Response Team,Beijing,China

4. Beijing Electronic Science and Technology Institute,Beijing,China

Funder

Youth Innovation Promotion Association, CAS

Publisher

IEEE

Link

Reference26 articles.

1. Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification;hu;ArXiv Preprint,2021

2. Distributed representations of sentences and docu-ments;le;International Conference on Machine Learning,0

4. Calibrate before use: Improving few-shot performance of language models;zhao;ArXiv Preprint,2021

5. Bert: Pretraining of deep bidirectional transformers for language understanding;devlin;ArXiv Preprint,2018

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