Papers by Zizhao 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. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation (2025.acl-long)
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| Challenge: | Language models evolve to tackle complex, multifaceted tasks, requiring granular evaluations . recent studies have focused on leaderboard and benchmark results, but limited interpretability makes it difficult to compare strengths and weaknesses of models. |
| Approach: | They propose an unsupervised tree-structured diagnosis framework for understanding model proficiency in specific abilities with an LLM as a judge. |
| Outcome: | The proposed framework improves model in-context learning and predicts model weaknesses with a 55% success rate compared to the framework without SkillVerse. |
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context (2026.acl-long)
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| Challenge: | Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents. |
| Approach: | They propose a framework that separates tactical execution, strategic oversight, and context organization into three specialized components. |
| Outcome: | The proposed framework improves accuracy by 20% relative to baselines on GAIA, BrowseComp, and Humanity’s Last Exam tasks. |
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. |