Search Results for author:
Found 5 papers, 2 papers with code
Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency
With the availability of various instruction datasets, a pivotal challenge is how to effectively select and integrate these instructions to fine-tune large language models (LLMs).
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale.
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
Obstacle-Transformer: A Trajectory Prediction Network Based on Surrounding Trajectories
Therefore, this paper proposes an Obstacle-Transformer to predict trajectory in a constant inference time.
Lyapunov-Based Reinforcement Learning State Estimator
An actor-critic reinforcement learning algorithm is proposed to learn the state estimator approximated by a deep neural network.
