Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

This paper introduces a pioneering methodology in autonomous robot control, denoted as
LLM-BRAIn, enabling the generation of adaptive behaviors in robots in response to operator …

Pre-trained large language models (LLMs) require fine-tuning to improve their
responsiveness to natural language instructions. Federated learning (FL) offers a way to …

The scaling laws have become the de facto guidelines for designing large language models
(LLMs), but they were studied under the assumption of unlimited computing resources for …

We propose a neural language modeling system based on low-rank adaptation (LoRA) for
speech recognition output rescoring. Although pretrained language models (LMs) like BERT …

This paper introduces LLM-MARS, first technology that utilizes a Large Language Model
based Artificial Intelligence for Multi-Agent Robot Systems. LLM-MARS enables dynamic …

As large language models (LLMs) are applied to more use cases, creating high quality, task-
specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high …

The segment anything model (SAM), a foundational model designed for promptable
segmentation tasks, demonstrates exceptional generalization capabilities, making it highly …

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of Large
Language Models (LLMs) to various downstream applications. However, the effectiveness of …

General networks for 3D medical image segmentation have recently undergone extensive
exploration. Behind the exceptional performance of these networks lies a significant demand …