Papers by Bohan Yao
Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL (2026.findings-acl)
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Zhewei Yao, Guoheng Sun, Łukasz Borchmann, Zheyu Shen, Minghang Deng, Bohan Zhai, Hao Zhang, Ang Li, Yuxiong He
| Challenge: | Translating natural language questions into SQL is a core challenge in natural language understanding and human-computer interaction. |
| Approach: | They propose a reinforcement learning framework and model family to generate accurate, executable SQL using a lightweight reward signal based solely on execution correctness. |
| Outcome: | The proposed framework outperforms previous versions of 70B-class systems and achieves state-of-the-art execution accuracy across six diverse Text2SQL benchmarks. |
Optimizing Reasoning for Text-to-SQL with Execution Feedback (2025.findings-acl)
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| Challenge: | Large language models excel in many reasoning tasks, but their ability to leverage Chain-of-Thought (CoT) reasoning remains underexplored. |
| Approach: | They propose a framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-poly DPO, relying solely on execution accuracy as feedback. |
| Outcome: | The proposed framework improves execution accuracy on BIRD and Spider datasets. |
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)
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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang
| Challenge: | TableLLM is a robust large language model capable of handling tabular data manipulation tasks. |
| Approach: | They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy. |
| Outcome: | The proposed model has 8 billion parameters and is capable of handling tabular data tasks. |
Diverse Multi-tool Aggregation with Large Language Models for Enhanced Math Reasoning (2025.findings-emnlp)
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| Challenge: | Multi-TAG uses multiple tools to solve complex math problems over multiple reasoning steps. |
| Approach: | They propose a tool-based LLM framework that leverages multiple tools to solve math problems. |
| Outcome: | The proposed framework outperforms baselines that use individual tools with the same number of runs and significantly outperformed standard baselines. |