Papers by Mingyang Pan
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)
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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
| Challenge: | Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries. |
| Approach: | They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions. |
| Outcome: | The proposed framework improves the performance of the proposed benchmark by 28.2%. |