2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Training Report of TeleChat3-MoE
Xinzhang Liu, Chao Wang, Zhihao Yang +51
TeleChat3-MoE is the latest series of TeleChat large language models, featuring a Mixture-of-Experts (MoE) architecture with parameter counts ranging from 105 billion to over one t…
T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables
Jie Zhang, Changzai Pan, Kaiwen Wei +12
Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information…
TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
Sishi Xiong, Ziyang He, Zhongjiang He +6
While large language models (LLMs) have shown promise in the table question answering (TQA) task through prompt engineering, they face challenges in industrial applications, includ…
Technical Report of TeleChat2, TeleChat2.5 and T1
Zihan Wang, Xinzhang Liu, Yitong Yao +35
We introduce the latest series of TeleChat models: \textbf{TeleChat2}, \textbf{TeleChat2.5}, and \textbf{T1}, offering a significant upgrade over their predecessor, TeleChat. Despi…
TeleChat Technical Report
Zhongjiang He, Zihan Wang, Xinzhang Liu +33
In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language…