Papers by Yao Su
Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias (2024.emnlp-main)
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| Challenge: | Existing prompting methods that require white-box access to the model or substantial training fail to simultaneously lessen toxicity and bias. |
| Approach: | They propose a strategy that encourages LLMs to integrate diverse human perspectives and self-regulate their responses by incorporating diverse human viewpoints. |
| Outcome: | The proposed approach can significantly diminish toxicity (up to 89%) and bias (up 73%) in LLMs’ responses. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models (2024.emnlp-main)
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Zhihao Wang, Shiyu Liu, Jianheng Huang, Wang Zheng, YiXuan Liao, Xiaoxin Chen, Junfeng Yao, Jinsong Su
| Challenge: | Version updates are an indispensable requirement for Large Language Models . a large learning rate in the first stage and a complete learning decay process are crucial for version updates of LLMs. |
| Approach: | They propose a learning rate path switching training paradigm for version updates of Large Language Models. |
| Outcome: | The proposed paradigm reduces training cost to 58% when training four versions of LLMs compared to PTFS and CPT . |
Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024.findings-acl)
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| Challenge: | Existing studies on relation extraction ignore non-bridge entities, leading to bias during inference. |
| Approach: | They propose a graph-based cross-document Relation Extraction model with non-bridge entity enhancement and prediction debiasing that integrates non-cross entities with target entities and bridge entities. |
| Outcome: | The proposed model outperforms baseline models on open and closed datasets. |
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal (2024.acl-long)
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Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su
| Challenge: | Existing methods to train LLMs on previous training data are not feasible in real-world applications because of catastrophic forgetting. |
| Approach: | They propose a framework that uses the LLM to generate synthetic instances for rehearsal and refine the instance outputs based on the synthetic inputs. |
| Outcome: | The proposed framework achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient. |
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis (2025.findings-acl)
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Yicheng Lang, Kehan Guo, Yue Huang, Yujun Zhou, Haomin Zhuang, Tianyu Yang, Yao Su, Xiangliang Zhang
| Challenge: | Large Language Models (LLMs) have been used to remove harmful knowledge and undesirable capabilities. |
| Approach: | They propose a framework that leverages Cognitive Diagnosis Modeling to evaluate LLM unlearning. |
| Outcome: | The proposed framework enhances evaluation and facilitates removal of harmful abilities. |
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation (2023.findings-emnlp)
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| Challenge: | Existing methods to generate abstractive summarizations are slow and abstractive, but we propose a novel approach to enhance the level of abstractiveness without sacrificing the informativeness of generated summaries. |
| Approach: | They propose a novel approach to enhance the level of abstractiveness without sacrificing the informativeness of generated summaries by exposing diverse pseudo summary with two supervision to the student model. |
| Outcome: | The proposed method outperforms previous methods in abstractive summarization distillation, producing highly abstractive and informative summaries. |
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (2021.tacl-1)
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| Challenge: | Recent advances in open-domain question answering (ODQA) have led to human-level performance on many datasets. |
| Approach: | They provide a comprehensive and quantitative analysis about the difficulty of book QA . they compare the results of their research with extensive ODQA experiments . |
| Outcome: | The proposed model outperforms existing models on event-oriented questions on the NarrativeQA dataset. |
An Imitation Game for Learning Semantic Parsers from User Interaction (2020.emnlp-main)
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| Challenge: | Existing methods for learning semantic parsers are expensive and tedious . despite the widespread applications, bootstrapping and fine-tuning is tedious a task . |
| Approach: | They propose an alternative method for learning semantic parsers directly from users . they propose an annotation-efficient imitation learning algorithm that iteratively collects new datasets . |
| Outcome: | The proposed method is cost-effective and shows promising performance on the text-to-SQL problem. |
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine Translation (2022.findings-naacl)
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| Challenge: | Existing diverse NMT models lack translation diversity due to a discrepancy between training and inference . despite the success of diverse NTM, there is still a lack of translation diversity . |
| Approach: | They propose a multi-candidate optimization framework for diverse NMT to deal with this defect. |
| Outcome: | The proposed framework is transparent to basic diverse NMT models, and universally makes better trade-off between diversity and quality. |
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)
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| Challenge: | Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments. |
| Approach: | They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics. |
| Outcome: | The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas. |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings (2021.emnlp-main)
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Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, Jinsong Su
| Challenge: | Existing sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. |
| Approach: | They propose a novel sentence ordering framework which introduces two classifiers to make better use of pairwise orderings for graph-based sentence ordering. |
| Outcome: | The proposed model achieves state-of-the-art performance on five commonly-used datasets. |
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study (D19-1)
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| Challenge: | Existing semantic parsing technologies are not well-suited for use in real-world applications. |
| Approach: | They propose a model-based intelligent agent that generates a clarification question in natural language . they propose 'interactive semantic parsing' with a human user in the loop . |
| Outcome: | The proposed approach improves both parsing accuracy and user confidence . it is demonstrated on two text-to-SQL datasets with different state-of-the-art parsers . |
EmoTrans: Emotional Transition-based Model for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotions are causally transmitted among communication participants, facilitating comprehension of intricate changes in emotional states during the conversation. |
| Approach: | They propose an Emotional Transition-based Emotion Recognizer that captures ET features in an emotional conversation by concatenating the most recent utterances with their corresponding speakers. |
| Outcome: | The proposed model is sensitive to emotions and captures ET features in the sample. |
Towards User-Driven Neural Machine Translation (2021.acl-long)
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| Challenge: | a good translation should implicitly mirror user traits rather than translate the original content semantically. |
| Approach: | They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion . |
| Outcome: | The proposed framework can capture user traits from historical inputs under zero-shot learning fashion. |
Revisiting Non-Autoregressive Translation at Scale (2023.findings-acl)
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| Challenge: | Extensive experiments on two advanced NAT models show scaling can improve translation performance. |
| Approach: | They empirically examine the impact of scaling on NAT behaviors on a large-scale WMT dataset. |
| Outcome: | The proposed model can achieve comparable performance with the scaling model while maintaining the superiority of decoding speed with standard NAT models. |
Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)
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Suhang Wu, Jialong Tang, Chengyi Yang, Pei Zhang, Baosong Yang, Junhui Li, Junfeng Yao, Min Zhang, Jinsong Su
| Challenge: | Existing methods for terminology translation struggle with interference from irrelevant noise. |
| Approach: | They propose a Locate-and-Focus method that locates terminologies within utterances to construct translation knowledge by minimizing irrelevant information for ST models. |
| Outcome: | The proposed method locates terminologies within utterances and enhances the success rate of terminology translation while maintaining robust general translation performance. |