Yu Guo
2025
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
Yu Guo
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Dong Jin
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Shenghao Ye
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Shuangwu Chen
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Jianyang Jianyang
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Xiaobin Tan
Findings of the Association for Computational Linguistics: ACL 2025
Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models and their closed-source counterparts. In this paper, we introduce SQLForge, a novel approach for synthesizing reliable and diverse data to enhance text-to-SQL reasoning in LLMs. We improve data reliability through SQL syntax constraints and SQL-to-question reverse translation, ensuring data logic at both structural and semantic levels. We also propose an SQL template enrichment and iterative data domain exploration mechanism to boost data diversity. Building on the augmented data, we fine-tune a variety of open-source models with different architectures and parameter sizes, resulting in a family of models termed SQLForge-LM. SQLForge-LM achieves the state-of-the-art performance on the widely recognized Spider and BIRD benchmarks among the open-source models. Specifically, SQLForge-LM achieves EX accuracy of 85.7% on Spider Dev and 59.8% on BIRD Dev, significantly narrowing the performance gap with closed-source methods.
2020
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Pretraining Language Model
Shilei Liu
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Yu Guo
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BoChao Li
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Feiliang Ren
Proceedings of the Fourteenth Workshop on Semantic Evaluation
This paper introduces our system for commonsense validation and explanation. For Sen-Making task, we use a novel pretraining language model based architecture to pick out one of the two given statements that is againstcommon sense. For Explanation task, we use a hint sentence mechanism to improve the performance greatly. In addition, we propose a subtask level transfer learning to share information between subtasks.
2019
STAC: Science Toolkit Based on Chinese Idiom Knowledge Graph
Meiling Wang
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Min Xiao
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Changliang Li
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Yu Guo
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Zhixin Zhao
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Xiaonan Liu
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
Chinese idioms (Cheng Yu) have seen five thousand years’ history and culture of China, meanwhile they contain large number of scientific achievement of ancient China. However, existing Chinese online idiom dictionaries have limited function for scientific exploration. In this paper, we first construct a Chinese idiom knowledge graph by extracting domains and dynasties and associating them with idioms, and based on the idiom knowledge graph, we propose a Science Toolkit for Ancient China (STAC) aiming to support scientific exploration. In the STAC toolkit, idiom navigator helps users explore overall scientific progress from idiom perspective with visualization tools, and idiom card and idiom QA shorten action path and avoid thinking being interrupted while users are reading and writing. The current STAC toolkit is deployed at http://120.92.208.22:7476/demo/#/stac.
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- Shuangwu Chen 1
- Jianyang Jianyang 1
- Dong Jin 1
- Bochao Li 1
- Changliang Li 1
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