  This paper details the efforts of the WisPerMed team in the BioLaySumm2024
Shared Task on automatic lay summarization in the biomedical domain, aimed at
making scientific publications accessible to non-specialists. Large language
models (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned
and employed to create lay summaries from complex scientific texts. The
summarization performance was enhanced through various approaches, including
instruction tuning, few-shot learning, and prompt variations tailored to
incorporate specific context information. The experiments demonstrated that
fine-tuning generally led to the best performance across most evaluated
metrics. Few-shot learning notably improved the models' ability to generate
relevant and factually accurate texts, particularly when using a well-crafted
prompt. Additionally, a Dynamic Expert Selection (DES) mechanism to optimize
the selection of text outputs based on readability and factuality metrics was
developed. Out of 54 participants, the WisPerMed team reached the 4th place,
measured by readability, factuality, and relevance. Determined by the overall
score, our approach improved upon the baseline by approx. 5.5 percentage points
and was only approx 1.5 percentage points behind the first place.
