Large language models (LLMs) have emerged as powerful tools in chemistry, significantly
impacting molecule design, property prediction, and synthesis optimization. This review …

Transformers with linear attention allow for efficient parallel training but can simultaneously
be formulated as an RNN with 2D (matrix-valued) hidden states, thus enjoying linear-time …

[Background.] Empirical research in requirements engineering (RE) is a constantly evolving
topic, with a growing number of publications. Several papers address this topic using …

Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a
lightweight technique to encourage prompt-adherence in generations. In this work, we …

In recent years, transformer-based models have emerged as powerful tools for natural
language processing tasks, demonstrating remarkable performance in several domains …

The question-answering system for Life science research, which is characterized by the
rapid pace of discovery, evolving insights, and complex interactions among knowledge …

This paper presents a scholarly Knowledge Graph Question Answering (KGQA) that
answers bibliographic natural language questions by leveraging a large language model …

In the financial field, constructing efficient investment portfolios is a focal point of research,
encompassing asset selection and optimization of asset allocation. With the advancements …

Empirical software engineering research on production systems has brought forth a better
understanding of the software engineering process for practitioners and researchers alike …

Large Language Models (LLMs) have transformed NLP with their remarkable In-context
Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; …