Introduction: The application of natural language processing (NLP) for extracting data from biomedical research has gained momentum with the advent of large language models (LLMs). However, the effect of different LLM parameters, such as temperature settings, on biomedical text mining remains underexplored and a consensus on what settings can be considered 'safe' is missing. This study evaluates the impact of temperature settings on LLM performance for a named-entity recognition and a classification task in clinical trial publications. Methods: Two datasets were analyzed using GPT-4o and GPT-4o-mini models at nine different temperature settings (0.00-2.00). The models were used to extract the number of randomized participants and classified abstracts as randomized controlled trials (RCTs) and/or as oncology-related. Different performance metrics were calculated for each temperature setting and task. Results: Both models provided correctly formatted predictions for more than 98.7% of abstracts across temperatures from 0.00 to 1.50. While the number of correctly formatted predictions started to decrease afterwards with the most notable drop between temperatures 1.75 and 2.00, the other performance metrics remained largely stable. Conclusion: Temperature settings at or below 1.50 yielded consistent performance across text mining tasks, with performance declines at higher settings. These findings are aligned with research on different temperature settings for other tasks, suggesting stable performance within a controlled temperature range across various NLP applications.

P.W. has a patent application titled 'Method for detection of neurological abnormalities' outside of the submitted work. The remaining authors declare no conflict of interest.

https://github.com/windisch-paul/temperature

This study did not receive any funding

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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All data and code used for the analysis have been uploaded to https://github.com/windisch-paul/temperature. The dataset has also been submitted to Dryad and is currently undergoing review.

https://github.com/windisch-paul/temperature