Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Large Language Models (LLMs) are capable of displaying a wide range of abilities that are
not directly connected with the task for which they are trained: predicting the next words of …

Large language models (LLMs) have shown incredible performance in completing various
real-world tasks. The current knowledge learning paradigm of LLMs is mainly based on …

Large language models (LLMs) have shown substantial progress in natural language
understanding and generation, proving valuable especially in the medical field. Despite …

In Model-Driven Engineering (MDE), domain-specific modeling languages (DSMLs) play a
key role to model systems within specific application domains. Creating DSMLs is a …

Recent advances in Large Language Models (LLMs) have revolutionized numerous fields,
including Qualitative Data Analysis (QDA). This paper introduces a novel method …

The increasing demand for transparent and reliable models, particularly in high-stakes
decision-making areas such as medical image analysis, has led to the emergence of …

Recommending products to users with intuitive explanations helps improve the system in
transparency, persuasiveness, and satisfaction. Existing interpretation techniques include …

Large language models (LLMs) have shown incredible performance in completing various
real-world tasks. The current paradigm of knowledge learning for LLMs is mainly based on …

In this study, we address the challenge of consistently following emotional support strategies
in long conversations by large language models (LLMs). We introduce the Strategy …