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- surveyDecember 2024
Tool Learning with Foundation Models
- Yujia Qin,
- Shengding Hu,
- Yankai Lin,
- Weize Chen,
- Ning Ding,
- Ganqu Cui,
- Zheni Zeng,
- Xuanhe Zhou,
- Yufei Huang,
- Chaojun Xiao,
- Chi Han,
- Yi Ren Fung,
- Yusheng Su,
- Huadong Wang,
- Cheng Qian,
- Runchu Tian,
- Kunlun Zhu,
- Shihao Liang,
- Xingyu Shen,
- Bokai Xu,
- Zhen Zhang,
- Yining Ye,
- Bowen Li,
- Ziwei Tang,
- Jing Yi,
- Yuzhang Zhu,
- Zhenning Dai,
- Lan Yan,
- Xin Cong,
- Yaxi Lu,
- Weilin Zhao,
- Yuxiang Huang,
- Junxi Yan,
- Xu Han,
- Xian Sun,
- Dahai Li,
- Jason Phang,
- Cheng Yang,
- Tongshuang Wu,
- Heng Ji,
- Guoliang Li,
- Zhiyuan Liu,
- Maosong Sun
ACM Computing Surveys (CSUR), Volume 57, Issue 4Article No.: 101, Pages 1–40https://doi.org/10.1145/3704435Humans possess an extraordinary ability to create and utilize tools. With the advent of foundation models, artificial intelligence systems have the potential to be equally adept in tool use as humans. This paradigm, which is dubbed as tool learning with ...
- tutorialJuly 2024
Empowering Large Language Models: Tool Learning for Real-World Interaction
SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 2983–2986https://doi.org/10.1145/3626772.3661381Since the advent of large language models (LLMs), the field of tool learning has remained very active in solving various tasks in practice, including but not limited to information retrieval. This half-day tutorial provides basic concepts of this field ...
- research-articleJuly 2024
Exploring Universal Intrinsic Task Subspace for Few-Shot Learning via Prompt Tuning
- Yujia Qin,
- Xiaozhi Wang,
- Yusheng Su,
- Yankai Lin,
- Ning Ding,
- Jing Yi,
- Weize Chen,
- Zhiyuan Liu,
- Juanzi Li,
- Lei Hou,
- Peng Li,
- Maosong Sun,
- Jie Zhou
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 32Pages 3631–3643https://doi.org/10.1109/TASLP.2024.3430545Why can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find evidence indicating that the adaptations of PLMs to various few-shot tasks ...
- research-articleApril 2024
Moderate-fitting as a natural backdoor defender for pre-trained language models
- Biru Zhu,
- Yujia Qin,
- Ganqu Cui,
- Yangyi Chen,
- Weilin Zhao,
- Chong Fu,
- Yangdong Deng,
- Zhiyuan Liu,
- Jingang Wang,
- Wei Wu,
- Maosong Sun,
- Ming Gu
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing SystemsArticle No.: 80, Pages 1086–1099Despite the great success of pre-trained language models (PLMs) in a large set of natural language processing (NLP) tasks, there has been a growing concern about their security in real-world applications. Backdoor attack, which poisons a small number of ...
- research-articleAugust 2020
Improving Sequence Modeling Ability of Recurrent Neural Networks via Sememes
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 28Pages 2364–2373https://doi.org/10.1109/TASLP.2020.3012060Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit sememes in specific tasks and few efforts are made to utilize sememes more ...