Papers by Qingyun Jin
SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Task (2025.coling-main)
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| Challenge: | Existing efforts to bolster LLMs’ proficiency in Cypher generation are hindered by the lack of annotated datasets of Query-Cypher pairs. |
| Approach: | They propose a method for constructing a synthetic Query-Cypher pair dataset using LLM prompting and template-filling. |
| Outcome: | The proposed method enhances the performance of LLMs on Text2Cypher task via SFT. |
Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. |
| Approach: | They propose a method for converting multi-head attention into grouped-query attention with any compression ratio of KV heads. |
| Outcome: | The proposed method can compress up to 87.5% KV heads of LLaMA2-7B model and 75% Kv heads of Sheared-LLa MA-1.3B with acceptable performance degradation. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |