Search Results for author: Yixin Lian

Found 6 papers, 5 papers with code

Learning to Check: Unleashing Potentials for Self-Correction in Large Language Models

1 code implementation20 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.

Mathematical Reasoning

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

1 code implementation8 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.

Arithmetic Reasoning

Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification

1 code implementation26 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.

Contrastive Learning Few-shot HTC +2

Detecting Log Anomalies with Multi-Head Attention (LAMA)

no code implementations7 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.

Anomaly Detection

MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation

3 code implementations12 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.

Dialogue State Tracking Natural Language Understanding +1

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