Papers by Wenxuan Shi
Hierarchical Memory Organization for Wikipedia Generation (2025.acl-long)
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Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing methods for generating Wikipedia articles do not utilize memory directly for outline generation. |
| Approach: | They propose a method to generate Wikipedia articles autonomously by leveraging a hierarchical memory architecture. |
| Outcome: | The proposed framework outperforms baseline methods in producing informative and reliable articles. |
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback (2023.findings-emnlp)
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Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu
| Challenge: | Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field. |
| Approach: | They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field . |
| Outcome: | The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field . |
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)
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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song
| Challenge: | Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning. |
| Approach: | They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized. |
| Outcome: | The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized . |
QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize. |
| Approach: | They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options. |
| Outcome: | The proposed framework outperforms baseline approaches while using only 33% of the synthetic data. |
Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)
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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo
| Challenge: | Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. |
| Approach: | They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark. |
| Outcome: | The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness. |
Identifying the Achilles’ Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026.findings-acl)
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Wenxuan Wang, Yuk-Kit Chan, Zixuan Ling, Shi Juluan, Youliang Yuan, Jen-tse Huang, Yifei Zhang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu
| Challenge: | Current methods for evaluating LLMs’ veracity are limited by the need for extensive human labor, test data contamination, or limited scope, hindering efficient and effective exposure of errors. |
| Approach: | They propose a framework that extracts fact triplets to generate diverse question types using rule-based natural language processing techniques. |
| Outcome: | The proposed framework can trigger factual errors in up to 55% of questions in large LLMs while maintaining coverage of questions. |
Learning to Ask: When LLM Agents Meet Unclear Instruction (2025.emnlp-main)
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Wenxuan Wang, Shi Juluan, Zixuan Ling, Yuk-Kit Chan, Chaozheng Wang, Cheryl Lee, Youliang Yuan, Jen-tse Huang, Wenxiang Jiao, Michael R. Lyu
| Challenge: | Despite their impressive capabilities, LLMs struggle with complex computations and delivering accurate, timely information. |
| Approach: | They propose a framework that prompts LLM agents to ask questions when they encounter obstacles due to unclear instructions and an automated evaluation tool called ToolEvaluator. |
| Outcome: | The proposed framework outperforms existing frameworks for tool learning in the Noisy ToolBench. |
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis (2022.acl-long)
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| Challenge: | Structured sentiment analysis is a dependency parsing problem, with imbalanced label distributions and large text spans. |
| Approach: | They propose a novel labeling strategy which contains two sets of token pair labels . they propose tuple extraction problem with a more balanced label distribution . |
| Outcome: | The proposed model outperforms existing models on 5 benchmark datasets in four languages. |
Entity-centered Cross-document Relation Extraction (2022.emnlp-main)
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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai
| Challenge: | Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents. |
| Approach: | They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%. |
When One LLM Drools, Multi-LLM Collaboration Rules (2026.acl-long)
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Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi, Yulia Tsvetkov
| Challenge: | a single general-purpose LLM is not enough to produce a reliable output, argues this paper . a multi-LLM collaboration approach addresses reliability, democratization, and pluralism . |
| Approach: | They argue that a single general-purpose LLM is not enough to produce a reliable output . they organize existing multi-LLM collaboration methods into a hierarchy based on access and information exchange . |
| Outcome: | The proposed method addresses reliability, democratization, and pluralism challenges a single LLM fails to produce a reliable output. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
Teaching LLMs to Abstain across Languages via Multilingual Feedback (2024.emnlp-main)
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Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding, Orevaoghene Ahia, Shuyue Stella Li, Vidhisha Balachandran, Sunayana Sitaram, Yulia Tsvetkov
| Challenge: | Existing studies on LLM abstention focus on English, but they show that it can reduce the accuracy of the model by 20.5% . |
| Approach: | They propose to teach LLMs to abstain in the face of knowledge gaps by generating multiple feedback items in related languages. |
| Outcome: | Extensive experiments show that the proposed approach outperforms baselines and achieves 9.2% improvement for low-resource languages. |
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder . |
| Approach: | They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies . |
| Outcome: | The proposed approach improves translation performance and model robustness on three language pairs. |
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)
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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song
| Challenge: | Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations. |
| Approach: | They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering. |
| Outcome: | Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks. |
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (2020.acl-main)
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| Challenge: | Creating job requirements is a crucial step in the recruiting process, but it is difficult to specify the level of education, experience, relevant skills per the job description. |
| Approach: | They propose a conditional text generation task to generate job requirements based on job descriptions . they use a hierarchical decoder to label the job description with multiple skills . a skill knowledge graph is constructed to capture the global prior knowledge about skills based upon the model . |
| Outcome: | The proposed method is evaluated on real-world job posting data. |