Yutao Mou (牟宇滔)

I am a third-year master student at PRIS Lab, Beijing University of Posts and Telecommunications, and supervised by Weiran Xu. I received the B.S. degree from Beijing University of Posts and Telecommunications, in 2021.

I have been working on building safe, reliable and scalable artificial intelligence systems. My main research interests include natural language processing and dialogue system, especially out-of-distribution (OOD) generalization, representation learning, and pre-training. Recently, I focus on large language models, such as LLM safety and alignment.

Feel free to contact me for communication and collaboration.

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Publications

This paper first propose a new paradigm: knowledge rekindle, which aims to re-incorporate the fine-tuned expert model into the training cycle and break through the performance upper bounds of experts without introducing additional annotated data. Then we further propose a unified expert-guided pre-training (UEGP) framework for knowledge rekindle.

This paper focuses on the generalized intent discovery task, and proposes a decoupled prototype learning framework (DPL) to decouple pseudo label disambiguation and representation learning.

This paper focuses on new intent discovery and clustering task, and propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents. Specifically, we design a novel K-nearest neighbor contrastive learning objective (KCL) for in-domain pre-training, and a hard negative mining strategy for self-supervised representation learning on unlabeled out-of-domain data.

This paper focuses on out-of-domain (OOD) intent detection task, and propose a unified neighborhood learning framework (UniNL) to detect OOD intents. propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents. Specifically, we design a K-nearest neighbor contrastive learning objective for training and introduce a KNN-based scoring function for confidence estimation. We aim to align training objective with confidence function in inference stage.

This paper defines a new task, Generalized Intent Discovery (GID), which aims to extend an IND intent classifier to an open-world intent set including IND and OOD intents. We hope to simultaneously classify a set of labeled IND intent classes while discovering and recognizing new unlabeled OOD types incrementally. We construct three public datasets for different application scenarios and propose two kinds of frameworks, pipeline-based and end-to-end for future work.

Discovering Out-of-Domain (OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is how to transfer prior IND knowledge to OOD clustering. This paper proposes a decoupled knowledge transfer framework for new intent discovery and clustering, which unifies the two-stage learning process of in-domain and out-of-domain data into instance discrimination and clustering discrimination tasks and bridges the gap between in-domain and out-of-domain data.

Internships

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Service