Papers by Zizheng Zhang
Multilingual and Multi-Aspect Hate Speech Analysis (D19-1)
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| Challenge: | Current research on hate speech analysis is oriented towards monolingual and single classification tasks. |
| Approach: | They propose to use a multilingual multi-aspect hate speech analysis dataset to test current methods . they evaluate the dataset in various classification settings and discuss how to leverage annotations . |
| Outcome: | The proposed dataset can be used to improve hate speech detection and classification in general. |
ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
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| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |
Cloze Quality Estimation for Language Assessment (2023.findings-eacl)
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| Challenge: | Cloze tests are widely used in language proficiency tests, but they suffer from low quality and low reliability. |
| Approach: | They propose a task to evaluate whether a cloze test is of sufficient "high-quality" they use a dataset that includes English clozing tests and corresponding evaluations by native English speakers. |
| Outcome: | The proposed method could contribute to the CQE task, but the task is still challenging. |
OASIS: Order-Augmented Strategy for Improved Code Search (2025.acl-long)
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Gao Zuchen, Zizheng Zhan, Xianming Li, Erxin Yu, Haotian Zhang, Chenbin Chenbin, Yuqun Zhang, Jing Li
| Challenge: | Code embeddings capture the semantic representations of code and are crucial for various code-related large language model (LLM) applications. |
| Approach: | They propose an order-augmented strategy for improved code search that leverages order-based similarity labels to capture subtle differences in similarity among negative pairs. |
| Outcome: | The proposed model outperforms state-of-the-art models focusing on major positive-negative differences. |
Global Constraints with Prompting for Zero-Shot Event Argument Classification (2023.findings-eacl)
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| Challenge: | Existing zero-shot trigger extraction models require annotations, which is not practical for open-domain applications. |
| Approach: | They propose to use global constraints with prompting to tackle event argument classification without annotation and task-specific training. |
| Outcome: | The proposed model outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1 without given argument spas. |
Tree-of-Quote Prompting Improves Factuality and Attribution in Multi-Hop and Medical Reasoning (2025.emnlp-main)
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Justin Xu, Yiming Li, Zizheng Zhang, Augustine Yui Hei Luk, Mayank Jobanputra, Samarth Oza, Ashley Murray, Meghana Reddy Kasula, Andrew Parker, David W Eyre
| Challenge: | Large language models (LLMs) produce fluent but factually incorrect outputs, a phenomenon commonly referred to as hallucination. |
| Approach: | They propose a Tree-of-Quote framework that decomposes complex questions into subquestions and generates quotes to support each step without retrieval. |
| Outcome: | Experiments on StrategyQA, 2WikiMultiHopQA, MuSiQue, MoreHopQ, and MedQA show that ToQ improves factuality and attribution over baselines. |