Papers by Yueqi Song
GlobalBench: A Benchmark for Global Progress in Natural Language Processing (2023.emnlp-main)
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Yueqi Song, Simran Khanuja, Pengfei Liu, Fahim Faisal, Alissa Ostapenko, Genta Winata, Alham Aji, Samuel Cahyawijaya, Yulia Tsvetkov, Antonios Anastasopoulos, Graham Neubig
| Challenge: | despite advances in NLP, significant disparities in performance across languages still exist . prior benchmarks focused on a limited number of tasks and languages, but now GlobalBench tracks progress on all languages. |
| Approach: | They propose to use global benchmarks to track progress on all NLP datasets in all languages. |
| Outcome: | a new tool tracks progress on all NLP datasets in all languages and tracks per-speaker utility and equity . globalbench is designed to identify the most under-served languages and reward research efforts . a globalbech is available at https://github.com/neulab/globalbench. |
Beyond Browsing: API-Based Web Agents (2025.findings-acl)
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| Challenge: | Existing web agents use browsers to facilitate human activities such as online shopping, online planning, and other work-related tasks. |
| Approach: | They propose to use web browsers as an interface to interact with online content through application programming interfaces (APIs) they propose to call APIs and use Hybrid Agents to perform online tasks. |
| Outcome: | The proposed agents outperform web Browsing Agents on a widely-used and realistic benchmark for web navigation tasks. |
What Is Missing in Multilingual Visual Reasoning and How to Fix It (2025.findings-naacl)
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| Challenge: | NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. |
| Approach: | They propose a translation-test approach to tackle multilinguality, visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. |
| Outcome: | The proposed interventions boost open models LLaVA-v1.5-13B by 13.4%, LLva-v1.6-34B by 20.3%, and Qwen-VL by 16.7% while minorly improving GPT-4V’s performance. |
Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics (2025.emnlp-main)
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Jiarui Liu, Yueqi Song, Yunze Xiao, Mingqian Zheng, Lindia Tjuatja, Jana Schaich Borg, Mona T. Diab, Maarten Sap
| Challenge: | a study of multi-dimensional persona effects in AI-AI debates shows that personas influence moral stances and debate outcomes . political ideology and personality traits exert the strongest influence, according to our study . |
| Approach: | They propose to use a 6-dimensional persona space to simulate structured debates . they find political ideology and personality traits exert the strongest influence . |
| Outcome: | The study shows that personas affect moral stances and debate outcomes . political ideology and personality traits exert the strongest influence . |
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. |
Grounding Multilingual Multimodal LLMs With Cultural Knowledge (2025.emnlp-main)
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| Challenge: | a new data-centric approach could address cultural gaps in multimodal large language models . despite being trained on billions of image-text pairs, today's models are biased towards English and Western data. |
| Approach: | They propose a data-centric approach that directly grounds MLLMs in cultural knowledge. |
| Outcome: | The proposed approach outperforms open-source models on cultural-focused benchmarks without degrading results on mainstream vision–language tasks. |
An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance (2024.emnlp-main)
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| Challenge: | a new task is to translate images to make them culturally relevant . currently, translation systems focus on translating words and images . |
| Approach: | They propose a task of translating images to make them culturally relevant . they build pipelines comprising state-of-the-art generative models to do the task . |
| Outcome: | The proposed pipelines can translate only 5% of translated images for some countries and no translation is successful for others. |