Papers by Jiaxiang Chen
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences. |
| Approach: | They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach . |
| Outcome: | The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results. |
From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)
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| Challenge: | Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction. |
| Approach: | They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. |
| Outcome: | The proposed model outperforms strong baselines on the Big-Bench Hard benchmark. |
Smart “Chef”: Verifying the Effect of Role-based Paraphrasing for Aspect Term Extraction (2023.findings-emnlp)
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| Challenge: | Aspect Term Extraction (ATE) is a task of automatically extracting aspect terms from sentences. |
| Approach: | They propose to automatically rewrite sentences from virtual experts with different roles . they leverage ChatGPT to determine virtual experts in the considered domains . |
| Outcome: | The proposed method can be used to expand the predictions obtained on the original sentences without retraining or fine-tuning the baseline extractors. |
AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents (2025.acl-demo)
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Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Pang Xin, Zhucong Li, Xu Yinghui
| Challenge: | a new framework automates deployment and debugging of AI projects . complexity of environment configurations, dependency conflicts, and debuggering issues hinder scalability and adoption. |
| Approach: | They propose an end-to-end framework that automates AI project deployment . they conducted experiments on 30 AI deployment cases to evaluate its effectiveness . |
| Outcome: | The proposed framework reduces deployment time and improves success rates by reducing human intervention. |
FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making (2025.findings-emnlp)
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| Challenge: | Large language models often overlook key behavioral patterns underlying human financial behavior. |
| Approach: | FinHEAR is a multi-agent framework for human expertise and Adaptive Risk-aware reasoning. |
| Outcome: | FinHEAR outperforms baseline models in trend forecasting and decision-making. |