Papers by Shi Zong
L2Dir: Integrating L_2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval (2026.acl-long)
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Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu, Jungang Xu
| Challenge: | Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process. |
| Approach: | They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly. |
| Outcome: | The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks. |
Extracting a Knowledge Base of COVID-19 Events from Social Media (2022.coling-1)
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| Challenge: | a flood of COVID-19 related information has appeared on social media since December 2019 . this includes reports on public figures who have tested positive/negative for the virus . |
| Approach: | They construct a corpus of 10,000 tweets with annotated public reports of five COVID-19 events, using slot-filling questions to fill in slots. |
| Outcome: | The proposed method can be quickly applied to develop knowledge bases for new domains in response to emerging crises, including natural disasters or future disease outbreaks. |
Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media (N19-1)
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| Challenge: | 6,000 tweets describe software vulnerabilities, which are shared across a range of websites and social media platforms. |
| Approach: | They propose a method to link software vulnerabilities reported in tweets to CVEs in the National Vulnerability Database (NVD) a Precision@50 of 0.86 is achieved when forecasting high severity vulnerabilities, they show . |
| Outcome: | The proposed method outperforms baseline methods based on tweet volume and the language used to describe them online. |
Measuring Forecasting Skill from Text (2020.acl-main)
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| Challenge: | Prior studies have shown that some individuals can make accurate predictions with consistently better accuracy. |
| Approach: | They examine linguistic factors associated with people's predictions including uncertainty, readability, and emotion. |
| Outcome: | The proposed model can accurately predict forecasting skill using only language. |
Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)
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| Challenge: | Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints. |
| Approach: | They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media. |
| Outcome: | The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method. |
Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)
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| Challenge: | Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities. |
| Approach: | They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities. |
| Outcome: | The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models. |
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)
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| Challenge: | In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. |
| Approach: | They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models. |
| Outcome: | The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images. |
FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs (2026.acl-long)
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Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang, Songlin Hu, Xiaodan Zhang, Zhongjiang Yao
| Challenge: | Large Language Models (LLMs) have enhanced or replaced traditional non-player characters in video games. |
| Approach: | They propose a benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. |
| Outcome: | The proposed benchmark assesses four bias types across transaction, cooperation, and competition using a novel metric, FairMCV. |
How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts? (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are used for question answering over long contexts . high computational costs and latency hinder the process . |
| Approach: | They explore the capabilities of Large Language Models to answer multiple questions based on the same conversational context. |
| Outcome: | The proposed models outperform proprietary and public models in question answering . their results show that they can be cost-effective and transparent . |