Search Results for author: Yutao Mou

Found 13 papers, 9 papers with code

Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection

no code implementations27 Feb 2024 Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

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.

Intent Detection Transfer Learning

Knowledge Editing on Black-box Large Language Models

1 code implementation13 Feb 2024 Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Yutao Mou, Jinxu Zhao, Weiran Xu

Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge without negatively influencing other knowledge.

knowledge editing

APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection

no code implementations20 Oct 2023 Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system.

Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT

1 code implementation16 Oct 2023 Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu

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.

In-Context Learning Intent Discovery

Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery

1 code implementation28 May 2023 Yutao Mou, Xiaoshuai Song, Keqing He, Chen Zeng, Pei Wang, Jingang Wang, Yunsen Xian, Weiran Xu

Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn.

Intent Discovery Pseudo Label +1

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

1 code implementation19 Oct 2022 Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection.

Contrastive Learning Out of Distribution (OOD) Detection +2

Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

1 code implementation17 Oct 2022 Yutao Mou, Keqing He, Pei Wang, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu

For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning.

Clustering Contrastive Learning +3

Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning

no code implementations17 Oct 2022 Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Yuanmeng Yan, Weiran Xu

In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.

Intent Detection Out of Distribution (OOD) Detection

Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation

1 code implementation COLING 2022 Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Weiran Xu

Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set.

Out of Distribution (OOD) Detection

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