Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Xian, Yunsen"'
The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence. However, inevitable random action, i.e. user randomly accesses an item among multiple candidates or clicks several
Externí odkaz:
http://arxiv.org/abs/2404.05342
Autor:
Wang, Pei, He, Keqing, Wang, Yejie, Song, Xiaoshuai, Mou, Yutao, Wang, Jingang, Xian, Yunsen, Cai, Xunliang, Xu, Weiran
Publikováno v:
LREC-COLING 2024
Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems. Previous methods address it by fine-
Externí odkaz:
http://arxiv.org/abs/2402.17256
Autor:
Chen, Zhen, Liu, Jingping, Yang, Deqing, Xiao, Yanghua, Xu, Huimin, Wang, Zongyu, Xie, Rui, Xian, Yunsen
Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (\emph{subject}, \emph{predicate}, \emph{object}) from a given sentence. Compared with general information extraction (IE), OpenIE poses more challenges for
Externí odkaz:
http://arxiv.org/abs/2401.11107
Large Language Models (LLMs), such as ChatGPT and GPT-4, are designed to provide useful and safe responses. However, adversarial prompts known as 'jailbreaks' can circumvent safeguards, leading LLMs to generate potentially harmful content. Exploring
Externí odkaz:
http://arxiv.org/abs/2311.08268
Autor:
Wang, Pei, He, Keqing, Mou, Yutao, Song, Xiaoshuai, Wu, Yanan, Wang, Jingang, Xian, Yunsen, Cai, Xunliang, Xu, Weiran
Publikováno v:
EMNLP2023, Findings
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection studies generally work on the assumption that plenty of labeled IND intents exist. In this paper, we focus on a more prac
Externí odkaz:
http://arxiv.org/abs/2310.13380
Autor:
Song, Xiaoshuai, He, Keqing, Wang, Pei, Dong, Guanting, Mou, Yutao, Wang, Jingang, Xian, Yunsen, Cai, Xunliang, Xu, Weiran
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems. Previous methods address them by fi
Externí odkaz:
http://arxiv.org/abs/2310.10176
Autor:
Zhu, Tinghui, Liu, Jingping, Liang, Jiaqing, Jiang, Haiyun, Xiao, Yanghua, Wang, Zongyu, Xie, Rui, Xian, Yunsen
Taxonomy expansion task is essential in organizing the ever-increasing volume of new concepts into existing taxonomies. Most existing methods focus exclusively on using textual semantics, leading to an inability to generalize to unseen terms and the
Externí odkaz:
http://arxiv.org/abs/2309.06105
Autor:
Zhu, Renyu, Han, Chengcheng, Qian, Yong, Sun, Qiushi, Li, Xiang, Gao, Ming, Cao, Xuezhi, Xian, Yunsen
We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of mul
Externí odkaz:
http://arxiv.org/abs/2309.02190
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs. However, traditional Pre-trained Language Models (PLMs) are directly pre-trained on large-scale natural
Externí odkaz:
http://arxiv.org/abs/2308.14436
Autor:
Wang, Keheng, Duan, Feiyu, Wang, Sirui, Li, Peiguang, Xian, Yunsen, Yin, Chuantao, Rong, Wenge, Xiong, Zhang
Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and the inability to access external knowledge, LLMs often come with incorr
Externí odkaz:
http://arxiv.org/abs/2308.13259