5 papers
Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
Shahin Atakishiyev, Housam K. B. Babiker, Jiayi Dai +8
Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next…
SF-Mamba: Rethinking State Space Model for Vision
Masakazu Yoshimura, Teruaki Hayashi, Yuki Hoshino +2
The realm of Mamba for vision has been advanced in recent years to strike for the alternatives of Vision Transformers (ViTs) that suffer from the quadratic complexity. While the re…
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
Masakazu Yoshimura, Teruaki Hayashi, Yota Maeda
An ecosystem of Transformer-based models has been established by building large models with extensive data. Parameter-efficient fine-tuning (PEFT) is a crucial technology for deplo…
Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models
Teruaki Hayashi, Hiroki Sakaji, Jiayi Dai +1
Developing the capacity to effectively search for requisite datasets is an urgent requirement to assist data users in identifying relevant datasets considering the very limited ava…
Extraction of Research Objectives, Machine Learning Model Names, and Dataset Names from Academic Papers and Analysis of Their Interrelationships Using LLM and Network Analysis
S. Nishio, H. Nonaka, N. Tsuchiya +6
Machine learning is widely utilized across various industries. Identifying the appropriate machine learning models and datasets for specific tasks is crucial for the effective indu…