Papers by Xiang Long
VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection (2026.acl-long)
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| Challenge: | Existing static analysis tools focus on functional correctness and depend heavily on manual rules. |
| Approach: | They propose a framework that generates executable Traversal Detection Patterns (TDPs) to help detect hardware vulnerabilities. |
| Outcome: | The proposed framework improves the F1 score by 133% compared to LLM-based methods. |
Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)
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| Challenge: | Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated. |
| Approach: | They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models. |
| Outcome: | The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform. |
Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis (2024.findings-emnlp)
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Jianxiang Yu, Zichen Ding, Jiaqi Tan, Kangyang Luo, Zhenmin Weng, Chenghua Gong, Long Zeng, RenJing Cui, Chengcheng Han, Qiushi Sun, Zhiyong Wu, Yunshi Lan, Xiang Li
| Challenge: | Existing approaches to review scientific papers are limited by their content or quality . SEA is a framework for automated scientific review, but its contents are generic or partial. |
| Approach: | They propose a framework for automated scientific review using large language models . they propose to use a standardized review dataset to fine-tune an LLM to generate high-quality reviews. |
| Outcome: | The proposed framework can generate high-quality reviews from standardized datasets and improves on the existing feedback mechanisms. |
UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
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| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
Low Resource Style Transfer via Domain Adaptive Meta Learning (2022.naacl-main)
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| Challenge: | Existing unsupervised text style transfer methods suffer from performance degradation when fine-tuning the model in new domains. |
| Approach: | They propose a domain adaptive meta-learning approach with an adversarial style training approach for better content preservation and style transfer. |
| Outcome: | The proposed approach generalizes well on unseen low-resource domains against ten strong baselines. |
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction (2023.acl-long)
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Chen-Yu Lee, Chun-Liang Li, Hao Zhang, Timothy Dozat, Vincent Perot, Guolong Su, Xiang Zhang, Kihyuk Sohn, Nikolay Glushnev, Renshen Wang, Joshua Ainslie, Shangbang Long, Siyang Qin, Yasuhisa Fujii, Nan Hua, Tomas Pfister
| Challenge: | Existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. |
| Approach: | They propose a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. |
| Outcome: | The proposed model achieves state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. |