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. 2023 Oct 16;21:728. doi: 10.1186/s12967-023-04576-8

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Benchmarking of LLMs on gene scoring tasks. Correlation plots show the degree of similarity between scores generated for a statement by four LLMs: GPT-3.5, GPT-4, Claude and Bard. Scores were generated in triplicate for each model. Plots show scoring similarities and differences within and between models. Each panel shows correlations for scores on a given statement regarding: A Relevance to erythroid cells or erythropoiesis. B Use as a clinical biomarker. C Potential as a blood transcriptional biomarker. D Relevance to leukocyte immune biology. E Status as a known drug target. F Therapeutic relevance for immune-mediated diseases. Actual statements and prompts can be found in the Methods section (Step 3)