  Biomedical literature often uses complex language and inaccessible
professional terminologies. That is why simplification plays an important role
in improving public health literacy. Applying Natural Language Processing (NLP)
models to automate such tasks allows for quick and direct accessibility for lay
readers. In this work, we investigate the ability of state-of-the-art large
language models (LLMs) on the task of biomedical abstract simplification, using
the publicly available dataset for plain language adaptation of biomedical
abstracts (\textbf{PLABA}). The methods applied include domain fine-tuning and
prompt-based learning (PBL) on: 1) Encoder-decoder models (T5, SciFive, and
BART), 2) Decoder-only GPT models (GPT-3.5 and GPT-4) from OpenAI and BioGPT,
and 3) Control-token mechanisms on BART-based models. We used a range of
automatic evaluation metrics, including BLEU, ROUGE, SARI, and BERTscore, and
also conducted human evaluations. BART-Large with Control Token (BART-L-w-CT)
mechanisms reported the highest SARI score of 46.54 and T5-base reported the
highest BERTscore 72.62. In human evaluation, BART-L-w-CTs achieved a better
simplicity score over T5-Base (2.9 vs. 2.2), while T5-Base achieved a better
meaning preservation score over BART-L-w-CTs (3.1 vs. 2.6). We also categorised
the system outputs with examples, hoping this will shed some light for future
research on this task. Our code, fine-tuned models, and data splits are
available at \url{https://github.com/HECTA-UoM/PLABA-MU} \begin{IEEEkeywords}
Large Language Models, Text Simplification, Biomedical NLP, Control Mechanisms,
Health Informatics \end{IEEEkeywords}
