Bin Wang
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Scientist
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News
2025.03 A talk on AudioLLM evaluation will be given at Lorong AI. Check out the slides here.
2025.03 I will be joining Shanda AI Research Institute (SARI) as an AI Research Scientist working on LLMs in May. I will still based in Singapore and open for collaboration opportunities.
2025.01 Our AudioBench work is officially accepted to NAACL 2025 Main Conference! Leaderboard and Code.
2024.12 We released MERaLiON models, the first audio-based large language model designed specifically for Singlish and other related tasks!
2024-2026 Our team is working on National LLM Project Press Coverage.
2024 AudioBench is released with leaderboard and evaluation toolkit - AudioLLM Evaluation.
2024 NAACL SeaEval for Multilingual Evaluation.
About Me
I am a scientist at Aural & Language Intelligence Department, I2R, A*STAR. Before joing that, I was a research fellow at National University of Singapore (NUS) working with Prof. Haizhou Li from 2021-2023. I received my Ph.D. degree from University of Southern California (USC) supervised by Prof. C.-C. Jay Kuo in 2021. My bachelor's degree is obtained from University of Electronic Science and Technology of China (UESTC) in 2017.
Some of the topics that I am currently researching include:
Make LLM can hear - AudioLLM - Audio-Based Large Language Models
What techniques can be used to effectively integrate audio processing capabilities into existing LLM architectures?
What is the most efficient approach for achieving seamless cross-modality integration?
What benchmarks can be designed to accurately evaluate the real-world performance of AudioLLMs?
Current Outcomes: MERaLiON-AduioLLM, AudioBench, Awesome-Audio-LLM, MoWE-Audio
Multilingal and Multicultual LLM
What unique properties should a multilingual LLM possess to cater to diverse languages effectively?
How can multilingual learning be made more efficient and effective, especially for low-resource languages?
What internal mechanisms can ensure robust multilingual knowledge alignment within the model?
Conversional AI
Representation Learning for Retrieval-Augmented Generation, Knowledge Graphs
What representation and coordination strategies can enhance multi-agent communication in shared environments?
What methods can enable conversational agents to effectively reason and plan based on learned or provided world models?
Current Outcomes: Representation Learning, Commonsense Knowledge Graph
Opportunities
To be updated within SARI.
Some Publications
Bin Wang, Xunlong Zou, Geyu Lin, Shuo Sun, Zhuohan Liu, Wenyu Zhang, Zhengyuan Liu, AiTi Aw, Nancy F. Chen. “AudioBench: A Universal Benchmark for Audio Large Language Models.” NAACL, 2025. [paper], [code]
Bin Wang, Zhengyuan Liu, Xin Huang, Fangkai Jiao, Yang Ding, AiTi Aw, Nancy F. Chen. “SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning.” NAACL, 2024. [paper], [code]
Bin Wang, Chen Zhang, Yan Zhang, Yiming Chen and Haizhou Li. “Analyzing and Evaluating Faithfulness in Dialogue Summarization.” EMNLP, 2022. [paper], [code]
Bin Wang, C.-C. Jay Kuo, and Haizhou Li. “Just Rank: Rethinking Evaluation with Word and Sentence Similarities.” ACL, 2022. [paper], [code]
Bin Wang, Guangtao Wang, Jing Huang, Jiaxuan You, Jure Leskovec, and C.-C. Jay Kuo. “Inductive learning on commonsense knowledge graph completion.” IJCNN, 2021. [paper], [code]
Bin Wang, and C.-C. Jay Kuo. “SBERT-WK: A sentence embedding method by dissecting bert-based word models.” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020. [paper], [code]
Bin Wang*, Angela Wang*, Fenxiao Chen, Yuncheng Wang, and C.-C. Jay Kuo. “Evaluating word embedding models: methods and experimental results.” APSIPA transactions on signal and information processing, 2019. [paper], [code]