Papers by Yutao Mou
Exploiting domain-slot related keywords description for Few-Shot Cross-Domain Dialogue State Tracking (2022.emnlp-main)
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| Challenge: | Existing frameworks for dialogue state tracking with domain-slot-value labels are expensive . current models are limited due to high cost of data annotation and lack of data in some domains . |
| Approach: | They propose a framework based on domain-slot related description to tackle the challenge of few-shot cross-domain DST. |
| Outcome: | The proposed framework outperforms existing methods on MultiWOZ and gains strong slot accuracy compared to existing models. |
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery (2022.emnlp-main)
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| Challenge: | Existing methods for finding out-of-domain intents suffer from in-domain overfitting problem . previous methods fail to transfer prior knowledge to downstream clustering . |
| Approach: | They propose a unified K-nearest neighbor contrastive learning framework to discover OOD intents . they propose IND pre-training objective to learn discriminative features while maintaining intra-class diversity . |
| Outcome: | The proposed framework improves on three benchmark datasets. |
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning (2022.acl-short)
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| Challenge: | Existing methods to find out out-of-domain (OOD) intents do not take prior knowledge of in-domain data into account. |
| Approach: | They propose a disentangled knowledge transfer method to bridge the gap between IND pre-training and OOD clustering by using a unified multi-head contrastive learning framework. |
| Outcome: | The proposed method is able to group new unknown intents into different clusters, enabling future development of the system. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
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| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning (2022.emnlp-main)
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| Challenge: | Existing methods to detect out-of-domain (OOD) intents ignore alignment between representation learning and scoring function, limiting performance. |
| Approach: | They propose a unified neighborhood learning framework to detect OOD intents . they propose to align representation learning with scoring function . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback (2026.findings-acl)
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| Challenge: | Unlike chatbots, autonomous agents act directly on external environments, making tool invocation safety critical for reliable deployment. |
| Approach: | They develop a benchmark for step-level tool invocation safety detection in LLM agents and a guardrail model that proactively detects unsafe tool invoking actions before execution using multi-task reinforcement learning. |
| Outcome: | The proposed model reduces harmful tool invocations of ReAct-style agents by 65% on average and improves benign task completion by 10% under prompt injection attacks. |
Can You Really Trust Code Copilot? Evaluating Large Language Models from a Code Security Perspective (2025.acl-long)
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| Challenge: | Existing code security benchmarks focus on one task and paradigm, such as code completion and generation, without comprehensive assessment across dimensions like secure code generation, vulnerability repair and discrimination. |
| Approach: | They propose a multi-task benchmark for comprehensive evaluation of LLM code security . they also propose VC-Judge, an improved judgment model that aligns closely with human experts . |
| Outcome: | The proposed model can evaluate LLM-generated programs for vulnerabilities in a more efficient and reliable way. |
Value type: the bridge to a better DST model (2023.findings-acl)
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| Challenge: | Value type of the slots can provide lots of useful information for DST tasks. however, it has been ignored in most previous works. |
| Approach: | They propose a new framework for DST task based on slot value type . they propose to extract the type of token from each turn and train a Ner model to extract corresponding type-entity from each conversation according to the token. |
| Outcome: | The proposed framework is effective on two multi-domain task-oriented conversation datasets. |
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT (2023.emnlp-main)
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Xiaoshuai Song, Keqing He, Pei Wang, Guanting Dong, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods to fine-tune discriminative models address these challenges by focusing on in-domain intents. |
| Approach: | They evaluate ChatGPT on OOD intent discovery and generalized intent discovery tasks . they outline the strengths and weaknesses of ChatGPt and outline their results . |
| Outcome: | The proposed task aims to extend a closed intent classifier to open-world intent sets. |
Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognition (2023.findings-emnlp)
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| Challenge: | Currently, the generalized intent classification system only considers one stage of OOD learning and requires all IND data for joint training. |
| Approach: | They propose a task that detects OOD intents from dynamic OOD data streams . they propose CGID method that bootstraps new intent discovery through class prototypes . |
| Outcome: | The proposed task can detect out-of-domain (OOD) queries and extend them to the in-domain classifier . it can safely and efficiently detect out of-domain queries and avoid wrong operations . |
Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection (2024.lrec-main)
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Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song, Yutao Mou, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Out-of-domain (OOD) intent detection is crucial for task-oriented dialogue systems. |
| Approach: | They conduct a comprehensive evaluation of large language models (LLMs) under various experimental settings and outline their strengths and weaknesses. |
| Outcome: | The proposed models exhibit strong zero-shot and few-shot capabilities, but is still at a disadvantage compared to models fine-tuned with full resource. |
Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery (2023.acl-long)
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| Challenge: | Existing methods for generalized intent discovery lack pseudo label disambiguation and representation learning. |
| Approach: | They propose a prototype learning framework to decouple pseudo label disambiguation and representation learning. |
| Outcome: | The proposed method can decouple pseudo label disambiguation and representation learning. |
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection (2023.findings-emnlp)
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Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song, Yanan Wu, Jingang Wang, Yunsen Xian, Xunliang Cai, Weiran Xu
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are hard to label . previous studies use labeled in-domain data to learn intent representations . |
| Approach: | They propose a prototypical pseudo-labeling method for few-shot OOD detection . they propose 'protoOOD' framework and adaptive pseudo-labeled method . |
| Outcome: | The proposed method is able to detect out-of-domain (OOD) intents from user queries. |
Thinking Twice Makes Large Language Models Safer and More Helpful (2026.findings-acl)
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| Challenge: | Existing safety alignment techniques for large language models (LLMs) struggle to balance harmlessness and usefulness. |
| Approach: | They propose a safety-aware reflection-based reasoning framework that internalizes self-reflective reasoning and encourages reflection and correction. |
| Outcome: | The proposed framework outperforms reasoning-based alignment methods in safety alignment. |
UEGP: Unified Expert-Guided Pre-training for Knowledge Rekindle (2024.findings-naacl)
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Yutao Mou, Kexiang Wang, Jianhe Lin, Dehong Ma, Jun Fan, Daiting Shi, Zhicong Cheng, Gu Simiu, Dawei Yin, Weiran Xu
| Challenge: | Existing paradigms for pre-training and fine-tuning have limitations . knowledge rekindle aims to break through performance upper bounds of experts without introducing additional annotated data. |
| Approach: | They propose a new paradigm for pre-training and fine-tuning that aims to re-incorporate the fine- tuned expert model into the training cycle and break through performance upper bounds of experts. |
| Outcome: | The proposed model breaks through performance upper bounds of experts without additional annotated data. |
Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation (2022.coling-1)
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| Challenge: | Existing methods for OOD detection are overconfident for OD samples . lack of labeled OOD examples leads to poor prior knowledge about these unknown intents, making it challenging to detect OOD samples. |
| Approach: | They propose a Bayesian OOD detection framework to calibrate distribution uncertainty using Monte-Carlo Dropout. |
| Outcome: | The proposed framework gains 33.33% OOD F1 improvements with increasing only 0.41% inference time compared to previous methods. |