1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
Bo-Wen Zhang, Liangdong Wang, Ye Yuan +24
In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their…
cs.AI2024★ 1 cited
FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models
Ziqiang Yuan, Kaiyuan Wang, Shoutai Zhu +4
Large Language models (LLMs) usually rely on extensive training datasets. In the financial domain, creating numerical reasoning datasets that include a mix of tables and long text…
cs.AI2023
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
Wendi Li, Wei Wei, Xiaoye Qu +4
Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, va…