Papers by Zhengyuan Zhu
A Dashboard for Mitigating the COVID-19 Misinfodemic (2021.eacl-demos)
Copied to clipboard
Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li
| Challenge: | a new public dashboard aims to understand the impact of the COVID-19 misinfodemic on Twitter . the dashboard uses a curated catalog of COVId-19 related facts and debunks of misinformation . |
| Approach: | They propose a public dashboard that matches tweets with COVID-19 misinformation . they also propose experiments to analyze the spread of misinformation on twitter . |
| Outcome: | The proposed dashboard uses a curated catalog of COVID-19 related facts and debunks misinformation . it shows the most prevalent information from the catalog among Twitter users in user-selected geographic regions . |
LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media (2025.findings-acl)
Copied to clipboard
| Challenge: | Social media's global reach and ease of use have transformed how millions of users exchange opinions, news, and factual claims in real-time, making it fertile ground for misinformation. |
| Approach: | They propose a framework that leverages large language models to construct taxonomies of factual claims from social media by generating topics at multiple levels of granularity. |
| Outcome: | The proposed framework produces clear, coherent, and comprehensive taxonomies on three diverse datasets and outperforms other frameworks in most metrics. |
BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)
Copied to clipboard
Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen, Jiayu Li, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively. |
| Approach: | They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts. |
| Outcome: | The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts. |
RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts Toward Factual Claims (2025.findings-naacl)
Copied to clipboard
| Challenge: | Social media provides a valuable lens for assessing public perceptions and opinions. |
| Approach: | They propose a method that leverages large language models with retrieval-augmented generation to analyze tweets in relation to claims. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a new dataset . it shows that it outperformed existing methods and achieves a significant increase in Macro-F1 score on TSD-CT. |
Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs (2023.emnlp-main)
Copied to clipboard
| Challenge: | Graph-to-text models trained on small-scale datasets or datasets with limited variety of graph shapes are not adequate for more realistic large-scale, open-domain settings. |
| Approach: | They propose a novel approach that, given a graph-sentence pair in GraphNarrative, trims the sentence to eliminate portions that are not present in the corresponding graph. |
| Outcome: | The proposed model can be trained on existing datasets and is available on github. |
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors (2026.acl-long)
Copied to clipboard
Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Large language models (LLMs) perform well on table tasks, but they still make data referencing errors (DREs) prior studies have only offered limited, small-scale analyses. |
| Approach: | They propose inference-time strategies and lightweight critics to mitigate data referencing errors. |
| Outcome: | The proposed model achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-difference DREs and assists inference for larger models. |
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL (2026.acl-long)
Copied to clipboard
Harper Hua, Zhen Han, Zhengyuan Shen, Meng-Chieh Lee, Sheng Guan, Qi Zhu, Sullam Jeoung, Yueyan Chen, Yunfei Bai, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Recent large language models (LLMs) have significantly improved Text-to-SQL generation, but a gap remains between AI systems and human experts on challenging benchmarks such as BIRD-Sql. |
| Approach: | They propose a multi-turn reinforcement learning agentic framework for Text-to-SQL that uses execution feedback to iteratively refine its predictions. |
| Outcome: | The proposed framework outperforms proprietary systems on 7B and 14B models by **5% on average, underscoring the effectiveness of interactive, agentic workflows for robust Text-to-SQL generation. |
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering (2025.coling-demos)
Copied to clipboard
| Challenge: | Recent advances in retrieval-augmented generation have demonstrated impressive performance on the question-answering task. |
| Approach: | They propose a retrieval-augmented generation framework that generates an initial text answer and retrieves multimodal data relevant to the snippets of the initial text. |
| Outcome: | The proposed framework can be easily integrated into an enterprise chatbot to produce multimodal answers with minimal modifications. |