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I am a first-year CS PhD student at
Northwestern University,
advised by the wonderful
Manling Li.
I got my bachelor's degree with the Baosteel Award at
Gaoling School of AI, RUC.
I was fortunate to work with
Heng Ji
at UIUC and collaborate with fantastic teams at
DeepSeek.
Email /
Github /
Semantic Scholar /
Zhihu /
CV
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I work on various topics regarding Large Language Models, including interaction, alignment, and long-context understanding (retrieval). My representative works include (1) general interaction, e.g., MINT interaction benchmark, (2) the cross-application of LLM & IR, e.g., retrieval augmented models (RetaLLM) and LM-based IR, (3) efficient alignment of LLMs, e.g., expert-specialized fine-tuning. |
See full list on Semantic Scholar (Why I Love Semantic Scholar, and You Might Too)
Semantic Scholar uses AI-powered tools to summarize papers, highlight key phrases, and rank research by influence. This helps you find important studies faster. Its Semantic Reader helps you understand papers with skimming highlights and citation cards . You can also see how papers connect with citation graphs. While Google Scholar is great for broad searches, Semantic Scholar is smarter for finding high-quality and impactful research!
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Zihan Wang, Deli Chen, Damai Dai, Runxin Xu, Zhuoshu Li, Yu Wu EMNLP 2024 [paper] [code] 
We harness the Specialized Power of Experts in MoE LLMs through ESFT. By fine-tuning Down to 5% Experts in a layer, near-full performance can be achieved. |
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Xingyao Wang*, Zihan Wang*, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, Heng Ji ICLR 2024 [paper] [website] [code] 
We introduce MINT, a benchmark for evaluating LLMs in Multi-turn Interactions with tools and language feedback. MINT reveals several limitations in existing RLHF and SIFT methods on multi-turn interaction. |
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DeepSeek AI (157 authors including Zihan Wang) [paper] [code] 
DeepSeek-V2 is a strong MoE model with 23B activated parameters. It achieves stronger performance compared to DeepSeek 67B, saving 42.5% training costs and boosting generation by up to 5.76x. |
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Zihan Wang, Yujia Zhou, Yiteng Tu, Zhicheng Dou. CIKM 2023, Oral Presentation [paper] [code] 
We propose learnable NOVO document-IDs for model-based IR. NOVO IDs consist of non-overlapping n-gram sets to identify documents, optimized through denoising queries and retrieval tasks. |
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Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen [paper] [code] 
We develop a Retreival-Augmented LLM toolkit for better interaction between LLMs and retrieval systems. Feature modules: request rewriting, passage extraction, and fact-checking. |
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Website design from Jon Barron |