Papers by Chengwei Wei
CoinMath: Harnessing the Power of Coding Instruction for Math LLM (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. |
| Approach: | They propose a learning strategy to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. |
| Outcome: | The proposed learning strategy outperforms its baseline model, MAmmoTH, which uses code-based solutions. |
Resilience of Large Language Models for Noisy Instructions (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are powerful tools for interpreting human commands and generating text. |
| Approach: | They examine the resilience of large language models against five common types of disruptions including ASR, OCR, grammatical errors, typographical errors and distractive content. |
| Outcome: | The models show resistance to noise, but their performance suffers . authors evaluated the models against five common types of disruptions based on their results . |
MeaeQ: Mount Model Extraction Attacks with Efficient Queries (2023.emnlp-main)
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| Challenge: | Recent studies focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources. |
| Approach: | They propose a model extraction attack with efficient Queries that uses a zero-shot sequence inference classifier to filter task-relevant data from a public text corpus instead of a problem domain-specific dataset. |
| Outcome: | The proposed method achieves higher similarity to the victim model than baselines while requiring fewer queries. |
Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation (2024.emnlp-main)
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| Challenge: | Existing studies have tried to distill these capabilities into smaller language models (SLMs) however, these capabilities are often associated with more parameters, which is not practical to emergent in smaller models. |
| Approach: | They propose to decompose the traditional single-step learning process into two cascaded learning steps by restructuring the training objectives and concatenating the question with the rationale as input. |
| Outcome: | Extensive experiments show that the proposed method improves reasoning generalizability and diversity of the model. |
Capture the Key in Reasoning to Enhance CoT Distillation Generalization (2025.acl-long)
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| Challenge: | Existing distillation methods for Large Language Models (LLMs) focus on fine-tuning student SLMs on correct data, resulting in students struggling to learn the key instead of analyzing mistakes according to correct solutions. |
| Approach: | They propose a method that exposes key reasoning steps rather than simple fine-tuning students' CoTs data by using a set of prompts with similar reasoning paths but divergent conclusions. |
| Outcome: | The proposed method improves student SLMs' ability to learn key reasoning steps rather than fine-tuning them on teacher data. |
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability (2023.emnlp-main)
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| Challenge: | Neural network models are vulnerable to adversarial examples, and current methods based on adversarially transferable models rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model’s structural details. |
| Approach: | They propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. |
| Outcome: | The proposed approach achieves superior attack performance with small cost on ten datasets and demonstrates that it is a novel approach. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
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| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2025.acl-long)
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Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Mohamed Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo Sapan, Taki Hasan Rafi, Bin Wang, null Supryadi, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Aurora Izzani, Ayushman Singh, Evan Evan, Jauza Akbar Krito, Michael Anugraha, Fenal Ashokbhai Ilasariya, Haochen Li, John Amadeo Daniswara, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Can Udomcharoenchaikit, Fadil Risdian Ansori, Mahardika Krisna Ihsani, Giang Nguyen, Anab Maulana Barik, Dan John Velasco, Rifo Ahmad Genadi, Saptarshi Saha, Chengwei Wei, Isaiah Edri W. Flores, Kenneth Chen Ko Han, Anjela Gail D. Santos, Wan Shen Lim, Kaung Si Phyo, Tim Santos, Meisyarah Dwiastuti, Jiayun Luo, Jan Christian Blaise Cruz, Ming Shan Hee, Ikhlasul Akmal Hanif, M.Alif Al Hakim, Muhammad Rizky Sya’ban, Kun Kerdthaisong, Lester James Validad Miranda, Fajri Koto, Tirana Noor Fatyanosa, Alham Fikri Aji, Jostin Jerico Rosal, Jun Kevin, Robert Wijaya, Onno P. Kampman, Ruochen Zhang, Börje F. Karlsson, Peerat Limkonchotiwat
| Challenge: | Southeast Asia is underrepresented in vision-language research . SEA-VL is an open-source initiative dedicated to developing culturally relevant datasets for SEA languages. |
| Approach: | They propose to use crowdsourced, automated image crawling and synthetic image generation to develop culturally relevant datasets for SEA languages. |
| Outcome: | The proposed datasets capture SEA cultural nuances and contexts better than existing datasets. |