Search Results for author: Zichu Fei

Found 8 papers, 5 papers with code

LFKQG: A Controlled Generation Framework with Local Fine-tuning for Question Generation over Knowledge Bases

no code implementations COLING 2022 Zichu Fei, Xin Zhou, Tao Gui, Qi Zhang, Xuanjing Huang

Existing KBQG models still face two main challenges: (1) Most models often focus on the most relevant part of the answer entity, while neglecting the rest of the subgraph.

Natural Questions Question Generation +1

Iterative GNN-based Decoder for Question Generation

1 code implementation EMNLP 2021 Zichu Fei, Qi Zhang, Yaqian Zhou

However, (1) they ignore the rich structure information that is hidden in the previously generated text.

Question Generation Question-Generation +1

CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation

1 code implementation ACL 2022 Zichu Fei, Qi Zhang, Tao Gui, Di Liang, Sirui Wang, Wei Wu, Xuanjing Huang

CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.

Question Generation Question-Generation

RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms

no code implementations17 Oct 2023 Enyu Zhou, Rui Zheng, Zhiheng Xi, Songyang Gao, Xiaoran Fan, Zichu Fei, Jingting Ye, Tao Gui, Qi Zhang, Xuanjing Huang

Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors.

Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs

no code implementations6 Sep 2023 Chao Feng, Xinyu Zhang, Zichu Fei

In some previous works, additional modules like graph neural networks (GNNs) are trained on retrieved knowledge from external knowledge bases, aiming to mitigate the problem of lacking domain-specific knowledge.

Hallucination Knowledge Graphs +1

Uncertainty-Aware Label Refinement for Sequence Labeling

1 code implementation EMNLP 2020 Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li, Zichu Fei, Yeyun Gong, Xuanjing Huang

Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks.

Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features

1 code implementation18 Nov 2019 Tao Gui, Lizhi Qing, Qi Zhang, Jiacheng Ye, HangYan, Zichu Fei, Xuanjing Huang

In order to effectively reduce the impact of non-ideal auxiliary tasks on the main task, we further proposed a novel meta-learning-based multi-task learning approach, which trained the shared hidden layers on auxiliary tasks, while the meta-optimization objective was to minimize the loss on the main task, ensuring that the optimizing direction led to an improvement on the main task.

Clustering Data Augmentation +4

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