Papers by Tong Cui
TWAG: A Topic-Guided Wikipedia Abstract Generator (2021.acl-long)
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| Challenge: | Existing models view Wikipedia abstract as plain text, ignoring that it is a description of a certain entity and can be decomposed into different topics. |
| Approach: | They propose a model that guides Wikipedia abstract generation with topical information. |
| Outcome: | The proposed model outperforms baselines and is capable of generating comprehensive abstracts. |
ExpertIVS: Sociological Expert Driven Individual Value Simulation in Large Language Models (2026.findings-acl)
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Zhen Wang, Yuqi Ren, Yuehan Cui, Hongxiang Wang, Jianxiang Peng, Zhaoxia Zhang, Bingkun Zhu, Tongxuan Zhang, Dezhi Tong, Deyi Xiong
| Challenge: | Existing methods for social simulations mechanically stitch survey responses into prompts, which suffer from semantic fragmentation, failing to capture the internal coherence of human value systems. |
| Approach: | They propose a framework employing 14 Sociological Expert Agents to interpret World Values Survey responses through structured professional perspectives rather than direct responses concatenation. |
| Outcome: | Experiments on 480 individuals from 12 countries show that ExpertIVS outperforms baselines in value generalization and significantly outperfies the existing methods. |
Fine-grained Video Dubbing Duration Alignment with Segment Supervised Preference Optimization (2025.acl-long)
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| Challenge: | Video dubbing systems use neural machine translation and text-to-speech technologies to translate original speech into visual media programs. |
| Approach: | They propose a preference optimization method to optimize video dubbing duration alignment . they propose combining segment-wise sampling and fine-grained loss to mitigate duration mismatches . |
| Outcome: | The proposed method achieves superior performance in duration alignment tasks. |
Lost in Literalism: How Supervised Training Shapes Translationese in LLMs (2025.acl-long)
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| Challenge: | Large language models exhibit translationese errors and generate unexpected unnatural translations . Neural machine translation (NMT) has become the dominant method in machine translation research . |
| Approach: | They evaluate the prevalence of translationese in LLM-generated translations and investigate its roots during supervised fine-tuning. |
| Outcome: | The proposed methods reduce translationese while improving translation naturalness . the proposed methods are validated by human evaluations and automatic metrics . |
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)
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| Challenge: | Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated. |
| Approach: | They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses. |
| Outcome: | The proposed model outperforms baseline models on a large-scale MSC dataset. |
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)
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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li
| Challenge: | Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student. |
| Approach: | They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures. |