Papers by Jiang Shu
Crossing Variational Autoencoders for Answer Retrieval (2020.acl-main)
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| Challenge: | Existing methods learned semantic representations with dual encoders or dual variational auto-encoders failed to capture the aligned semantics between question and answer. |
| Approach: | They propose to use two variational auto-encoders to generate questions with aligned answers and generating answers with align questions. |
| Outcome: | The proposed method outperforms the state-of-the-art answer retrieval method on SQuAD. |
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts (2025.acl-long)
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Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang, Haoyuan Li, Zhelun Yu, Fangxun Shu, Weilong Dai, Hao Jiang, Fei Wu, Leilei Gan
| Challenge: | Existing studies on text-to-image (T2I) models focus on text alignment, image quality, and object composition capabilities. |
| Approach: | They propose a T2I-FactualBench benchmark to evaluate the factuality of knowledge-intensive concept generation. |
| Outcome: | The proposed framework evaluates the factuality of knowledge-intensive concept generation tasks. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)
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| Challenge: | Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent. |
| Approach: | They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video. |
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
| Challenge: | Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios . |
| Approach: | They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios. |
| Outcome: | The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm. |
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA (2026.acl-industry)
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Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
| Challenge: | Existing methods for QA in industrial environments are inherently relational and often updated. |
| Approach: | They propose a framework that optimizes retrieval and generation through two components: Graph-aware Retrieval and evidence-constrained reinforcement learning. |
| Outcome: | Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, safety, and URL validity. |
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |
Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training (2026.acl-industry)
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Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang
| Challenge: | Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet traditional singleround retrieval struggles with complex multistep reasoning. |
| Approach: | They propose a framework that introduces path-centric reward shaping for agentic RAG training. |
| Outcome: | The proposed framework improves on existing methods with an average accuracy gain of 7.7 points. |
IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences (2025.acl-long)
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| Challenge: | Existing models for document classification struggle with long-text processing due to quadratic computational complexity in the self-attention module. |
| Approach: | They propose a framework that utilizes retrieval to efficiently classify long documents . they use a quadratic attention matrix to capture dependencies between tokens in an input sequence . |
| Outcome: | The proposed framework excels in clinical note disease risk prediction tasks . it can process arbitrarily long documents without increasing computational cost and trainable on a single GPU. |
SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)
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| Challenge: | Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity. |
| Approach: | They propose a framework for the minimalist rectification of non-compliant image ads. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency. |
Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)
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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens
| Challenge: | a key intent behind many emails is to get a reply from the recipient. |
| Approach: | They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations. |
| Outcome: | The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates . |
ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)
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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu
| Challenge: | Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets. |
| Approach: | They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. |
| Outcome: | The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring . |