  Inferring over and extracting information from Large Language Models (LLMs)
trained on a large corpus of scientific literature can potentially drive a new
era in biomedical research, reducing the barriers for accessing existing
medical evidence. This work examines the potential of LLMs for dialoguing with
biomedical background knowledge, using the context of antibiotic discovery. The
systematic analysis is applied to ten state-of-the-art models, from models
specialised on biomedical scientific corpora to general models such as ChatGPT,
GPT-4 and Llama 2 in two prompting-based tasks: chemical compound definition
generation and chemical compound-fungus relation determination. The work
provides a systematic assessment on the ability of LLMs to encode and express
these relations, verifying for fluency, prompt-alignment, semantic coherence,
factual knowledge and specificity of generated responses. Results show that
while recent models have improved in fluency, factual accuracy is still low and
models are biased towards over-represented entities. The ability of LLMs to
serve as biomedical knowledge bases is questioned, and the need for additional
systematic evaluation frameworks is highlighted. The best performing GPT-4
produced a factual definition for 70% of chemical compounds and 43.6% factual
relations to fungi, whereas the best open source model BioGPT-large 30% of the
compounds and 30% of the relations for the best-performing prompt. The results
show that while LLMs are currently not fit for purpose to be used as biomedical
factual knowledge bases, there is a promising emerging property in the
direction of factuality as the models become domain specialised, scale-up in
size and level of human feedback.
