Papers by Zhengyuan Shi
A Dashboard for Mitigating the COVID-19 Misinfodemic (2021.eacl-demos)
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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 . |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
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| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Multilingual Neural RST Discourse Parsing (2020.coling-main)
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| Challenge: | Existing studies on text discourse parsing for English are limited due to the lack of annotated data. |
| Approach: | They propose to use multilingual vector representations and segment-level translation to establish a neural, cross-lingual discourse parser. |
| Outcome: | The proposed model achieves state-of-the-art on cross-lingual, document-level discourse parsing on all sub-tasks. |
Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs (2023.emnlp-main)
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| 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. |
DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)
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| Challenge: | Integration of large language models into electronic design automation has been a key driver in eDA. |
| Approach: | They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL. |
| Outcome: | The proposed model achieves state-of-the-art performance across all evaluated tasks. |
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)
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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao
| Challenge: | Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning. |
| Approach: | They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis. |
| Outcome: | The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. |
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023.emnlp-main)
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| Challenge: | Annotated data plays a critical role in training models and evaluating their performance. |
| Approach: | They propose a paradigm for Human-LLM co-annotation of unstructured texts at scale that utilizes uncertainty to estimate LLMs’ annotation capability. |
| Outcome: | The proposed model outperforms existing models on many text-annotation tasks with up to 21% performance improvement over random baseline. |
LDEDE: LRP-Driven Efficient Detection and Editing Framework for LLM Privacy Neurons (2026.findings-acl)
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Zhao Zhengyuan, Cao Lifeng, null Sunhaodong, Shi Haotian, Du Xuehui, Liu Aodi, Niu Lanjie, Yang Xiaocheng
| Challenge: | Existing privacy protection methods fail to cover context-dependent sensitive information and are prone to performance degradation. |
| Approach: | They propose a Layer-wise Relevance Propagation-driven framework for efficient privacy neuron detection and editing. |
| Outcome: | The proposed framework achieves 80% higher efficiency than gradient attribution methods while reducing leakage risks of Phone, Email, and medical privacy by 42.7%–73.5% on average and cutting computational time by 60%–90%. |