7 citations · 16 across the 5 of their papers we have counts for
5 papers
LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
Xingxuan Zhang, Gang Ren, Han Yu +35
We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents Limi…
COUNTS: Benchmarking Object Detectors and Multimodal Large Language Models under Distribution Shifts
Jiansheng Li, Xingxuan Zhang, Hao Zou +6
Current object detectors often suffer significant perfor-mance degradation in real-world applications when encountering distributional shifts. Consequently, the out-of-distribution…
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
Xingxuan Zhang, Haoran Wang, Jiansheng Li +6
Large language models (LLMs) like GPT-4 and LLaMA-3 utilize the powerful in-context learning (ICL) capability of Transformer architecture to learn on the fly from limited examples.…
On the Out-Of-Distribution Generalization of Multimodal Large Language Models
Xingxuan Zhang, Jiansheng Li, Wenjing Chu +6
We investigate the generalization boundaries of current Multimodal Large Language Models (MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specifi…
Embedding in Recommender Systems: A Survey
Maolin Wang, Xinjian Zhao, Wanyu Wang +9
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that conv…