1 code implementation • 20 Feb 2024 • Che Zhang, Zhenyang Xiao, Chengcheng Han, Yixin Lian, Yuejian Fang
After integrating the original CoT data and checking-correction data for training, we observe that models could improve their self-checking capabilities, thereby enhancing their self-correction capacity and eliminating the need for external feedback or ground truth labels to ascertain the endpoint of correction.
1 code implementation • 8 Oct 2023 • Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian, Xiang Li, Ming Gao, Baoyuan Wang
Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters.
1 code implementation • 14 Jun 2023 • Jingsheng Gao, Yixin Lian, Ziyi Zhou, Yuzhuo Fu, Baoyuan Wang
Open-domain dialogue systems have made promising progress in recent years.
1 code implementation • 26 May 2023 • Ke Ji, Yixin Lian, Jingsheng Gao, Baoyuan Wang
Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered.
no code implementations • 7 Jan 2021 • Yicheng Guo, Yujin Wen, Congwei Jiang, Yixin Lian, Yi Wan
Anomaly detection is a crucial and challenging subject that has been studied within diverse research areas.
3 code implementations • 12 Oct 2020 • Ting Han, Ximing Liu, Ryuichi Takanobu, Yixin Lian, Chongxuan Huang, Dazhen Wan, Wei Peng, Minlie Huang
In this paper, we introduce MultiWOZ 2. 3, in which we differentiate incorrect annotations in dialogue acts from dialogue states, identifying a lack of co-reference when publishing the updated dataset.