Papers by Henry Zou
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
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Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification (2023.emnlp-main)
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| Challenge: | Existing approaches to semi-supervised text classification suffer from pseudo-label bias and error accumulation. |
| Approach: | They propose a pseudo-labeling approach to semi-supervised text classification that unifies ideas from semi-semi-supervised learning and the task of learning with noise. |
| Outcome: | The proposed approach achieves a significant improvement on benchmark datasets even in the extremely-scarce-label setting. |
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank (2023.findings-emnlp)
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| Challenge: | Existing studies utilize social media platforms such as Twitter to build models for crisis event analysis, but semi-supervised approaches require annotating vast amounts of data and are impractical due to limited response time. |
| Approach: | They propose a method that stores and performs equal sampling for generated pseudo-labels from each class at each training iteration. |
| Outcome: | The proposed method performs better than existing methods in both in-distribution and out-of-difference settings. |
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks (2024.emnlp-main)
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| Challenge: | Existing research has shown that large language models have difficulty discerning the veracity of their intrinsic answers. |
| Approach: | They propose a jailbreak attack method that generates an aligned language model for malicious output. |
| Outcome: | The proposed method achieves competitive performance with more harmful outputs. |
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction (2024.findings-acl)
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Henry Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip Yu, Cornelia Caragea
| Challenge: | Existing datasets for attribute value extraction focus on explicit attribute values while neglecting the implicit ones. |
| Approach: | They present a multimodal dataset for implicit attribute value extraction that includes AVE and multimodality. |
| Outcome: | The proposed dataset includes 68k training and 1.6k testing data across five domains. |