11 citations · 24 across the 12 of their papers we have counts for
12 papers
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model
Sirui Wang, Peiguang Li, Yunsen Xian +1
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…
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection
Pei Wang, Keqing He, Yejie Wang +6
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…
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Pei Wang, Keqing He, Yutao Mou +6
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 plent…
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
Xiaoshuai Song, Keqing He, Pei Wang +6
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 tas…
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Guanting Dong, Rumei Li, Sirui Wang +3
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…
Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering
Keheng Wang, Feiyu Duan, Sirui Wang +5
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 t…
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Weihao Zeng, Keqing He, Yejie Wang +4
Pre-trained language models based on general text enable huge success in the NLP scenario. But the intrinsical difference of linguistic patterns between general text and task-orien…
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
Zhuocheng Gong, Jiahao Liu, Qifan Wang +6
While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use. Therefore, effectivel…
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery
Yutao Mou, Xiaoshuai Song, Keqing He +5
Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain and out-of-domain intents. The key challenges l…
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank
Jiduan Liu, Jiahao Liu, Qifan Wang +6
Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning…
Lifting the Curse of Capacity Gap in Distilling Language Models
Chen Zhang, Yang Yang, Jiahao Liu +4
Pretrained language models (LMs) have shown compelling performance on various downstream tasks, but unfortunately they require a tremendous amount of inference compute. Knowledge d…
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Xiang Li, Xiaojiang Zhou, Yao Xiao +4
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages. To account…