  The number of scientific articles published in the domain of food safety has
consistently been increasing over the last few decades. It has therefore become
unfeasible for food safety experts to read all relevant literature related to
food safety and the occurrence of hazards in the food chain. However, it is
important that food safety experts are aware of the newest findings and can
access this information in an easy and concise way. In this study, an approach
is presented to automate the extraction of chemical hazards from the scientific
literature through large language models. The large language model was used
out-of-the-box and applied on scientific abstracts; no extra training of the
models or a large computing cluster was required. Three different styles of
prompting the model were tested to assess which was the most optimal for the
task at hand. The prompts were optimized with two validation foods (leafy
greens and shellfish) and the final performance of the best prompt was
evaluated using three test foods (dairy, maize and salmon). The specific
wording of the prompt was found to have a considerable effect on the results. A
prompt breaking the task down into smaller steps performed best overall. This
prompt reached an average accuracy of 93% and contained many chemical
contaminants already included in food monitoring programs, validating the
successful retrieval of relevant hazards for the food safety domain. The
results showcase how valuable large language models can be for the task of
automatic information extraction from the scientific literature.
