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cs.CL2025
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
cs.CL2024★ 2 cited
HMoE: Heterogeneous Mixture of Experts for Language Modeling
An Wang, Xingwu Sun, Ruobing Xie +9
Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous…
cs.CL2024
Truth Forest: Toward Multi-Scale Truthfulness in Large Language Models through Intervention without Tuning
Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao +4
Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulne…