Papers by Ziqi Jin
Self-Harmonized Chain of Thought (2025.naacl-long)
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| Challenge: | Existing methods for chain-of-thought prompting have limitations . arithmetic, commonsense, and symbolic reasoning tasks are challenging . |
| Approach: | They propose a method that unifies diverse solution paths into a consistent reasoning pattern. |
| Outcome: | The proposed method outperforms existing methods by 2.8% on reasoning tasks. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) rely on English data for training, but are often not comparable across other languages. |
| Approach: | They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness . |
| Outcome: | The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination. |
Tab-CoT: Zero-shot Tabular Chain of Thought (2023.findings-acl)
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| Challenge: | Recent efforts to encourage more structured reasoning procedures to be captured have shown that chain-of-though (CoT) prompting methods can be effective in NLP tasks. |
| Approach: | They propose a tabular-format CoT prompting method that allows the complex reasoning process to be explicitly modeled in a highly structured manner. |
| Outcome: | The proposed method shows impressive performance improvements on a range of reasoning tasks. |