Papers by Lu Gan
PVTNL: Prompting Vision Transformers with Natural Language for Generalizable Person Re-identification (2025.findings-emnlp)
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| Challenge: | Domain generalization person re-identification (DG-ReID) aims to train models on source domains and generalize to unseen target domains. |
| Approach: | They propose a framework to generalize person re-identification using a vision-language model . body-part cues are used to segment images into semantically coherent regions . |
| Outcome: | The proposed framework can generalize to unseen domains and generalize semantics to people . it leverages the pre-trained vision-language model BLIP to extract aligned visual and textual embeddings. |
TrInk: Ink Generation with Transformer Network (2025.emnlp-main)
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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu
| Challenge: | Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples. |
| Approach: | They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies. |
| Outcome: | The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models. |
Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)
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| Challenge: | Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings. |
| Approach: | They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions. |
| Outcome: | The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset. |
LLaMA Pro: Progressive LLaMA with Block Expansion (2024.acl-long)
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| Challenge: | Existing studies have demonstrated that pre-trained LLMs are limited in certain domains, such as programming, mathematics, biomedical, or finance. |
| Approach: | They propose a new post-pretraining method with an expansion of Transformer blocks to tune the expanded blocks using only new corpus, efficiently and effectively improving the model’s knowledge while mitigating forgetting. |
| Outcome: | The proposed model outperforms existing models in programming and math and its instruction-following counterpart LLaMA Pro-8.3B in general tasks, programming, and mathematics. |
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective (2023.acl-long)
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| Challenge: | Existing approaches for information extraction (IE) are limited by the number of subtasks and the isolation of the subtask. |
| Approach: | They propose a new paradigm for universal information extraction that is compatible with any schema format and applicable to a list of IE tasks. |
| Outcome: | The proposed framework outperforms generative universal IE models on 14 benchmarks with the supervised setting and the state-of-the-art performance in low-resource scenarios. |
Re-examining the Role of Schema Linking in Text-to-SQL (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL models treat schema linking as a minor component . Existing solutions treat schema as merely a string component based on string matching . |
| Approach: | They build a schema linking corpus based on a Spider text-to-SQL dataset . they find schema linking is the crux for the current text- to-Sql task . |
| Outcome: | The proposed model performs well on the Spider text-to-SQL dataset despite its simplicity. |
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) models are crucial for academic writing . existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types . |
| Approach: | They propose to annotate 100 full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. |
| Outcome: | The proposed model can be used to identify 10 entity types in scientific articles . existing models cannot recognize fine-grained models like ML models and model architecture . |