7 papers
Learning to Orchestrate Agents under Uncertainty
Mary Chriselda Antony Oliver, Lan Jiang, Aaron Bundi Anampiu +3
Adaptive orchestration of heterogeneous agents requires making sequential delegation decisions under uncertain and evolving agent behaviour, e.g., coordinating specialised AI model…
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Mao Zheng, Zheng Li, Tao Chen +10
Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of wh…
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…
One-stop Training of Multiple Capacity Models
Lan Jiang, Haoyang Huang, Dongdong Zhang +2
Training models with varying capacities can be advantageous for deploying them in different scenarios. While high-capacity models offer better performance, low-capacity models requ…
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
Lan Jiang, Hao Zhou, Yankai Lin +3
Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. H…
On Length Divergence Bias in Textual Matching Models
Lan Jiang, Tianshu Lyu, Yankai Lin +3
Despite the remarkable success deep models have achieved in Textual Matching (TM) tasks, it still remains unclear whether they truly understand language or measure the semantic sim…