MUsE: Model of User-friendliness and Efficiency

At present, the sizes of large language models (LLMs) continue to increase as the number of trainable parameters increases. However, increased sizes make users pay higher costs for using LLMs, while the models are not user-friendly enough. Consequently, for socially vulnerable populations, such as children and older adults, the language models may be less accessible, and their responses may not be easy to understand. Therefore, we created a new LLM, termed the Model of User-friendliness and Efficiency (MUsE), and hypothesized that MUsE would surpass existing closed-source LLMs in user-friendliness and efficiency by facilitating conversations between LLMs and users based on the user type. MUsE leverages the Transformer architecture with light model size and low-rank adaptation (LoRA), which augments its efficiency by updating weights with lower dimensions. MUsE then undergoes user-friendly instruction tuning with a custom dataset created by a closed-source LLM. Through this process, it is efficiently trained and can outperform other LLMs in understandability, geniality, and applicability, contributing to better user-friendliness despite its small size. Code is available in https://github.com/seohyunqkr/MUsE.