Papers by Vincent Zhang
QueryForm: A Simple Zero-shot Form Entity Query Framework (2023.findings-acl)
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Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister
| Challenge: | Form-like document understanding is a key yet under-investigated problem . endlessly training specialized models on new document types is not scalable in many practical scenarios. |
| Approach: | They propose to use large-scale query-entity pairs generated from form-like webpages to pre-train QueryForm. |
| Outcome: | The proposed framework sets state-of-the-art average F1 score on XFUND and Payment benchmarks. |
LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)
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| Challenge: | Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness. |
| Approach: | They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights. |
| Outcome: | Extensive tests reveal weaknesses in LJP models and provide diagnostic insights. |
Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining (2020.lrec-1)
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| Challenge: | a large body of research has been done on aspect-based sentiment analysis (ABSA) for almost two decades . aspect-Based sentiment analysis is a task that extracts sentiment/opinions from text in terms of targets . |
| Approach: | They propose a meaning-preserving annotation scheme for aspect-based sentiment analysis . they then apply it to two popular ABSA datasets to examine their results . |
| Outcome: | The proposed approach improves the state of aspect-based sentiment analysis (ABSA) by preserving the meaning of the sentiment. |
AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering (2026.acl-long)
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Zheyuan Zhang, Kaiwen Shi, Zhengqing Yuan, Zehong Wang, Tianyi Ma, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing approaches to agent routing emphasize cost efficiency while overlooking the fine-grained contextual and relational structure inherent in QA tasks. |
| Approach: | They propose a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines while generalizing across benchmarks and LLM backbones. |
SParC: Cross-Domain Semantic Parsing in Context (P19-1)
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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences. |
| Approach: | They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences. |
| Outcome: | The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time. |
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)
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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown
| Challenge: | Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size. |
| Approach: | They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences. |
| Outcome: | The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size. |
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning (2025.acl-long)
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Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye
| Challenge: | Diet plays a critical role in human health, but tailoring dietary reasoning to individual health conditions remains a challenge. |
| Approach: | a new benchmark evaluates dietary reasoning using a national health survey data set. |
| Outcome: | The NGQA benchmark evaluates dietary reasoning across three tasks using a set of question complexity settings and baseline models. |
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)
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| Challenge: | a method for user targeting is developed to identify online users to whom an ad should be targeted. |
| Approach: | They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models. |
| Outcome: | The proposed method can increase positive and negative instances of positive training instances on two datasets. |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |
NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering (2026.eacl-long)
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Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye
| Challenge: | Existing methods for nutrition question answering face limited reasoning capacity and contextual overload . poor dietary patterns are associated with more than 11 million deaths in 2017 . |
| Approach: | They propose a framework that enables supervised multi-agent collaboration for nutritional QA. |
| Outcome: | The proposed framework outperforms single-agent and ensemble baselines in multi-agency reasoning tasks. |
InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)
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| Challenge: | InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Approach: | They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Outcome: | The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws. |
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
| Challenge: | Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. |
| Approach: | They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. |
| Outcome: | The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
LMDX: Language Model-based Document Information Extraction and Localization (2024.findings-acl)
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Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua
| Challenge: | Large Language Models have revolutionized Natural Language Processing but their application in extracting information from visually rich documents has not been successful. |
| Approach: | They propose a language model-based document information extraction and localization methodology to reframe the document information extract task for a LLM. |
| Outcome: | The proposed method enables extraction of singular, repeated, and hierarchical entities with and without training data. |
CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
| Approach: | They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. |
| Outcome: | Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art. |