Papers by Yu Mao
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue systems perform poorly on unseen topics due to limited topics covered in training data. |
| Approach: | They propose a language model that homogenizes different knowledge sources to a unified knowledge representation for knowledge-grounded dialogue generation tasks. |
| Outcome: | The proposed language model generalizes well across knowledge-grounded dialogue tasks. |
CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)
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| Challenge: | a number of tools are used to perform complex tasks, but the tool utilization process can cause errors. |
| Approach: | They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks. |
| Outcome: | The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios. |
“In-Dialogues We Learn”: Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning (2024.emnlp-main)
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| Challenge: | Existing approaches to personalized dialogue generate pre-defined profiles that are time-consuming and labor-intensive to create. |
| Approach: | They propose a framework that leverages dialogue history to characterize personas without pre-defined profiles. |
| Outcome: | The proposed framework improves BLEU and ROUGE scores on three datasets and human evaluations further validate the proposed method. |
One Unified Model for Diverse Tasks: Emotion Cause Analysis via Self-Promote Cognitive Structure Modeling (2025.naacl-long)
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| Challenge: | Existing models for emotion cause analysis overlook common ground rooted in cognitive emotion theories, in particular, the cognitive structure of emotions. |
| Approach: | They propose a unified model capable of tackling diverse emotion cause analysis tasks . they propose 'self-promote mechanism' that constructs the emotion cognitive structure through LLM . |
| Outcome: | The proposed model outperforms existing models and baselines on multiple emotion cause analysis tasks. |
Non-parallel Accent Transfer based on Fine-grained Controllable Accent Modelling (2023.findings-emnlp)
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| Challenge: | Existing accent transfer methods rely on parallel data or speech recognition models. |
| Approach: | They propose to use mutual information learning to disentangle accent features and control the accent of the generated speech during the inference time. |
| Outcome: | The proposed framework achieves superior performance to baseline models in accentedness and audio quality. |
Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)
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| Challenge: | Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem . |
| Approach: | They propose an EAE model with stage-customized entity type embedding to explore the role of entity types. |
| Outcome: | The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings. |
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis (2022.acl-demo)
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| Challenge: | M-SENA is an open-source platform for multimodal sentiment analysis. |
| Approach: | They propose to use a platform for multimodal sentiment analysis to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. |
| Outcome: | The proposed framework provides reliable benchmarks and baseline results of different modality features and MSA benchmarks. |
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)
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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev
| Challenge: | Existing models struggle with summarizing long text due to high memory complexity of the full self-attention. |
| Approach: | They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding. |
| Outcome: | The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
Skeletons Matter: Dynamic Data Augmentation for Text-to-Query (2025.emnlp-main)
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| Challenge: | Existing studies focus on a single query language, resulting in limited generalizability . a new task paradigm is proposed to unify semantic parsing tasks across different query languages . |
| Approach: | They propose a task paradigm that unifies parsing tasks across query languages . they identify query skeletons as a shared optimization target of Text-to-Query tasks . |
| Outcome: | The proposed method achieves state-of-the-art performance using only a small amount of synthesized data. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
BLADE: Benchmarking Language Model Agents for Data-Driven Science (2024.findings-emnlp)
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Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai Zhang, Lanyi Zhu, Mike Merrill, Jeffrey Heer, Tim Althoff
| Challenge: | Language model-based agents can be used to conduct and support data-driven science, but evaluating them on open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions. |
| Approach: | They propose a benchmark to automatically evaluate agents’ multifaceted approaches to open-ended research questions. |
| Outcome: | BLADE evaluates agents’ multifaceted approaches to open-ended research questions using data from 12 datasets and research questions drawn from existing scientific literature. |
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (2024.findings-acl)
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| Challenge: | Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios. |
| Approach: | They propose to use OCR/ASR-based generation to refine Chinese Spelling Correction models on random replacement-based corpora and filter them based on prediction confidence. |
| Outcome: | The proposed model outperforms existing models on three widely-used benchmarks while significantly alleviating over-correction. |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)
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| Challenge: | Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction. |
| Approach: | They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning. |
| Outcome: | The proposed agent performs well in both dialogue element modeling and out-of-domain tasks. |
Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)
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| Challenge: | Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs. |
| Approach: | They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal . |
| Outcome: | The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective . |
APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)
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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu
| Challenge: | Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement. |
| Approach: | They propose a method that involves tuning a small set of soft prompts for pre-trained language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark. |
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices. |
| Approach: | They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio. |
| Outcome: | The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed. |
A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression (2025.acl-long)
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| Challenge: | gist-based context compression methods can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, but it faces challenges in tasks like synthetic recall. |
| Approach: | They propose two strategies to improve gist-based context compression in large language models. |
| Outcome: | The proposed methods can achieve only slight performance loss on retrieval-augmented generation and long-document QA tasks, but they face challenges in tasks like synthetic recall. |
Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022.emnlp-main)
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| Challenge: | Existing models for multi-hop Question Answering have improved the implicit reasoning ability, but the black box nature of pure neural networks has hindered the construction of explainable intelligent systems. |
| Approach: | They propose a global differentiation strategy to explore optimal reasoning paths from latent probability space and a Dynamic Adaptive Reasoner to enhance generalization of unseen questions. |
| Outcome: | The proposed method achieves 17% improvements in F1-score against BreakRC and shows better interpretability. |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)
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| Challenge: | Pre-trained language models have improved dependency parsing accuracy in resource-rich languages . however, the accuracy drops sharply when the model is transferred to low-resource language . |
| Approach: | They propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones. |
| Outcome: | The proposed model outperforms baseline models on the benchmark datasets by 1.37 LAS and 1.34 UAS. |
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training (2023.findings-emnlp)
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Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
| Challenge: | Several perspectives of robustness for pre-trained language models have been studied independently, but lacking a unified consideration in multiple perspectives. |
| Approach: | They propose a technique to enhance the multi-perspective robustness of LMs by introducing adversarial perturbation while the model parameters are selectively updated upon their relative importance. |
| Outcome: | The proposed technique improves the robustness of LMs by incorporating four perspectives on model robustness. |
The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)
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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu
| Challenge: | Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations. |
| Approach: | They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions. |
| Outcome: | The proposed framework outperforms white-box methods and reduces computational overhead by over 90%. |
Solving the Right Problem is Key for Translational NLP: A Case Study in UMLS Vocabulary Insertion (2023.findings-emnlp)
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| Challenge: | a gap exists between research output and real-world task for automated NLP systems . a recent study shows that powerful models alone will not yield translational NLP solutions . |
| Approach: | They propose a formulation for UMLS vocabulary insertion which mirrors the real-world task . they propose measurable qualitative improvements to editors who carry out the UVI task based on strong datasets . |
| Outcome: | The proposed model outperforms existing models and improves the UVI task. |
Multilingual Knowledge Graph Completion via Efficient Multilingual Knowledge Sharing (2025.findings-emnlp)
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| Challenge: | Existing MKGC research ignores the shareability of cross-lingual knowledge. |
| Approach: | They propose a multilingual knowledge Graph Completion framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER). |
| Outcome: | The proposed framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits @3, and Hits_10 metrics, respectively, compared with existing state-of-the-art (SOTA) MKGC method. |