10 citations · 26 across the 13 of their papers we have counts for
6 papers · 1 filter
Generative Input: Towards Next-Generation Input Methods Paradigm
Keyu Ding, Yongcan Wang, Zihang Xu +4
Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of in…
Evaluating the Reversal Curse in Model Editing
Hao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling +3
Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensive, there has been a growing inte…
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Wayne Xin Zhao, Kun Zhou, Beichen Zhang +8
Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver,…
SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction
Jun-Yu Ma, Jia-Chen Gu, Zhen-Hua Ling +3
Zero-shot cross-lingual information extraction(IE) aims at constructing an IE model for some low-resource target languages, given annotations exclusively in some rich-resource lang…
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu +4
Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods r…
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding
Jia-Chen Gu, Zhen-Hua Ling, Quan Liu +2
Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention. However, existing methods on MPC understand…