5 citations · 15 across the 14 of their papers we have counts for
14 papers
AgentRefine: Enhancing Agent Generalization through Refinement Tuning
Dayuan Fu, Keqing He, Yejie Wang +7
Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial…
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
Yejie Wang, Keqing He, Dayuan Fu +11
Recently, there has been a growing interest in studying how to construct better code instruction tuning data. However, we observe Code models trained with these datasets exhibit hi…
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…
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
Yejie Wang, Keqing He, Guanting Dong +8
Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning approaches have been proposed to boost the code g…
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…
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation
Weihao Zeng, Lulu Zhao, Keqing He +4
Existing controllable dialogue generation work focuses on the single-attribute control and lacks generalization capability to out-of-distribution multiple attribute combinations. I…
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…
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…
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning
Yutao Mou, Pei Wang, Keqing He +4
Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-dom…
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery
Yutao Mou, Keqing He, Pei Wang +4
Discovering out-of-domain (OOD) intent is important for developing new skills in task-oriented dialogue systems. The key challenges lie in how to transfer prior in-domain (IND) kno…
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Weihao Zeng, Keqing He, Zechen Wang +8
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. However, such systems rely on costly manuall…
Generalized Intent Discovery: Learning from Open World Dialogue System
Yutao Mou, Keqing He, Yanan Wu +6
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes. But users may input out-of-domain (OOD) qu…
Unified Knowledge Prompt Pre-training for Customer Service Dialogues
Keqing He, Jingang Wang, Chaobo Sun +1
Dialogue bots have been widely applied in customer service scenarios to provide timely and user-friendly experience. These bots must classify the appropriate domain of a dialogue,…