Papers by Haotian Cui
CodeExp: Explanatory Code Document Generation (2022.findings-emnlp)
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Haotian Cui, Chenglong Wang, Junjie Huang, Jeevana Priya Inala, Todd Mytkowicz, Bo Wang, Jianfeng Gao, Nan Duan
| Challenge: | Existing code-to-text generation models produce only high-level code summaries that do not capture implementation-level choices essential for these scenarios. |
| Approach: | They propose a code explanation generation task that uses code docstrings to refine models. |
| Outcome: | The proposed model can generate well-structured long docstrings comparable to human-written ones. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
Object-oriented Neural Programming (OONP) for Document Understanding (P18-1)
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| Challenge: | Object-oriented Neural Programming (OONP) is a framework for semantically parsing documents in domains. |
| Approach: | They propose a framework for semantically parsing documents in specific domains using OONP . OOPN parsers use a rich family of operations to represent the semantics of the document . |
| Outcome: | The proposed framework can learn to handle fairly complicated ontology with training data of modest sizes. |