Papers by Wentao Ding
Dynamic Parallel Tree Search for Efficient LLM Reasoning (2025.acl-long)
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Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, Xianglong Liu, Dacheng Tao
| Challenge: | Recent methods focus on search accuracy while overlooking computational efficiency. |
| Approach: | They propose a parallelism framework that dynamically optimizes reasoning path in inference. |
| Outcome: | The proposed framework improves efficiency by 2-4 on average while maintaining or even surpassing existing reasoning algorithms in accuracy. |
Timeline-based Sentence Decomposition with In Context Learning for Temporal Fact Extraction (2024.acl-long)
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| Challenge: | Recent research on temporal fact extraction fails to establish time-to-fact correspondences in complex sentences. |
| Approach: | They propose a timeline-based sentence decomposition strategy using large language models with in-context learning to extract temporal facts from natural language text. |
| Outcome: | The proposed method achieves state-of-the-art on a complex temporal fact extraction dataset. |
Automatic rule generation for time expression normalization (2021.findings-emnlp)
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| Challenge: | Existing SOTA methods for normalization rely on expert-designed rules or grammars . current methods are domain sensitive and not sufficient on emerging corpora . |
| Approach: | They propose a method that generates normalization rules from annotated data without expert intervention. |
| Outcome: | The proposed method surpasses existing rule-based methods on the Tweets benchmark and on the TempEval-3 benchmark. |
Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for temporal question answering ignore intrinsic connections between events that can make them temporally related. |
| Approach: | They propose a temporal question answering method that generates query graphs by exploring relevant facts of mentioned entities. |
| Outcome: | The proposed method outperforms existing methods on two benchmarks over different knowledge graphs. |
Decoder Tuning: Efficient Language Understanding as Decoding (2023.acl-long)
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| Challenge: | Existing approaches to adapt pre-trained models with parameters frozen are based on input-side adaptation, which requires thousands of API queries. |
| Approach: | They propose to train a model-as-a-service (MaaS) setting to provide only the inference APIs for users . they argue that input-side adaptation could be arduous due to the lack of gradient signals . |
| Outcome: | The proposed model outperforms state-of-the-art algorithms with a 200x speed-up. |
AutoUE: Automated Generation of 3D Games in Unreal Engine via Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) and generative models have motivated studies on automated game generation from natural language descriptions. |
| Approach: | They propose a novel multi-agent system, AutoUE, which coordinates multiple agents to end-to-end generate 3D games, covering model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. |
| Outcome: | The proposed system covers model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. |