Improving In-Context Learning via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment Analysis-Reference-Cited by-同舟云学术

Improving In-Context Learning via Sequentially Selection and Preference Alignment for Few-Shot Aspect-Based Sentiment Analysis

Published:2024-07-10 Issue: Volume:33 Page:2462-2466
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Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wang Qianlong¹^ORCID,Ding Keyang¹^ORCID,Luo Xuan¹^ORCID,Xu Ruifeng²^ORCID

Affiliation:

1. Harbin Institute of Technology, Shenzhen, China

2. Harbin Institute of Technology & GD Prov. Key Lab. of Novel Security Intelligence Technologies, Shenzhen, China

Funder

National Natural Science Foundation of China

Shenzhen Foundational Research Funding

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

The Major Key Project of PCL

Natural Science Foundation of Guangdong

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657932

Reference30 articles.

1. Language Models Are Few-Shot Learners;Brown Tom;Advances in NeurIPS,2020

2. Hongjie Cai, Nan Song, Zengzhi Wang, Qiming Xie, Qiankun Zhao, Ke Li, Siwei Wu, Shijie Liu, Jianfei Yu, and Rui Xia. 2023. MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis. arXiv preprint arXiv:2306.16956 , Vol. 1 (2023), 1--14.

3. Zekai Chen, Mariann Micsinai Balan, and Kevin Brown. 2023. Language Models Are Few-Shot Learners for Prognostic Prediction. arXiv preprint arXiv:2302.12692 , Vol. 1 (2023), 1--9.

4. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Advances in NeurIPS Workshop on Deep Learning , Vol. 1 (2014), 1--9.

5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL. Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186.