Papers by Ziru Chen
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data (2025.emnlp-main)
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| Challenge: | supervised fine-tuning (SFT) data is critical for large multimodal models . current methods suffer from factual errors and hallucinations due to inadequate visual perception . |
| Approach: | They propose a pipeline that integrates vision priors and state-of-the-art LMMs with statistical methods to enhance SFT data quality. |
| Outcome: | The proposed pipeline outperforms existing models in text-rich reasoning tasks while enhancing reasoning efficiency. |
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024.acl-long)
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| Challenge: | Existing methods to build language agents that can plan efficiently and accurately have not met the needs of advanced planning methods to achieve such improvements. |
| Approach: | They propose to use iterative correction and tree search to solve multi-step problems in a language agent framework with three components: a generator, a discriminator, and a planning method. |
| Outcome: | The proposed methods improve performance on two tasks, text-to-SQL parsing and mathematical reasoning, while using discriminators with 90% accuracy. |
Tooling or Not Tooling? The Impact of Tools on Language Agents for Chemistry Problem Solving (2025.findings-naacl)
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Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, Huan Sun
| Challenge: | Existing evaluations of large language models (LLMs) with tools are limited and qualitative . existing evaluations have been limited and only focus on 14 tasks focusing on compound synthesis. |
| Approach: | They propose to develop an enhanced chemistry agent over ChemCrow to improve chemistry problem solving by integrating tools into LLMs. |
| Outcome: | The proposed agent does not consistently outperform its base LLMs without tools on specialized chemistry tasks and general chemistry questions. |
Text-to-SQL Error Correction with Language Models of Code (2023.acl-short)
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Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
| Challenge: | Existing semantic parsers are not accurate enough for use in text-to-SQL parsing tasks. |
| Approach: | They propose to build clause-level edit models to correct SQL queries instead of token-level ones. |
| Outcome: | The proposed model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. |
Exploring Chain of Thought Style Prompting for Text-to-SQL (2023.emnlp-main)
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| Challenge: | In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. |
| Approach: | They propose a new chain of thought prompting method that enhances LLMs’ reasoning ability through chain of thinking prompting, including the original chain-of-thought prompting and least-to-most prompting. |
| Outcome: | The proposed method brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gain, versus the least-to-most prompting. |
Automatic Evaluation of Attribution by Large Language Models (2023.findings-emnlp)
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| Challenge: | Generative large language models (LLMs) incorporate external references to generate and support claims. however, evaluating the attribution remains an open problem. |
| Approach: | They investigate automatic evaluation of attribution given by large language models . they define different types of attributed errors and then explore two approaches . |
| Outcome: | The proposed methods highlight promising signals and challenges. |
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)
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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun
| Challenge: | AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. |
| Approach: | They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. |
| Outcome: | The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages. |
Error Detection for Text-to-SQL Semantic Parsing (2023.findings-emnlp)
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| Challenge: | Existing text-to-SQL parsers are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. |
| Approach: | They propose a parser-independent error detection model for text-to-SQL semantic parsing . they use a language model of code as its bedrock and graph neural networks to learn structural features of queries . |
| Outcome: | The proposed model outperforms parser-dependent uncertainty metrics on three strong parsers . it could improve the performance and usability of text-to-SQL semantic parsing, it is shown . |