3 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
Zhiyong Wu, Zhenyu Wu, Fangzhi Xu +8
Existing efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are of…
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
Yinquan Lu, Wenhao Zhu, Lei Li +2
Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insuffi…
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Fangzhi Xu, Qiushi Sun, Kanzhi Cheng +3
One of the primary driving forces contributing to the superior performance of Large Language Models (LLMs) is the extensive availability of human-annotated natural language data, w…
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Fangzhi Xu, Zhiyong Wu, Qiushi Sun +6
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expre…
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
Fei Yuan, Yinquan Lu, WenHao Zhu +4
Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter i…