Configuration

GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)

Evidence concern: excluded from comparisons

Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A"

GPT-4o answering yes/no CRISPR screen questions with a 1-shot (+) prompt and no chain-of-thought instruction.

1 evaluation · 6 results

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Evaluations and results

1 evaluation · 6 results. Different protocols are not a single leaderboard.

Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit · Dataset: Virtual CRISPR Simple Benchmark

Sorted by F1 (Simple Benchmark) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	F1 (Simple Benchmark)	False positive rate (Simple Benchmark)	Negative predictive value (Simple Benchmark)	Precision (Simple Benchmark)	Recall (Simple Benchmark)	Specificity (Simple Benchmark)
GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)Evaluation	0.49	0.23	0.66	0.56	0.43	0.77

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.49 F1 (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "F1"
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.23 False positive rate (Simple Benchmark) fraction · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "FPR"
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.66 Negative predictive value (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "NPV"
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.56 Precision (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "PPV"
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.43 Recall (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "Sensitivity"
Configuration: GPT-4o, 1-shot (+), without chain-of-thought (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.77 Specificity (Simple Benchmark) fraction · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Evidence concern: excluded from comparisons Appendix A.2 describes the random baseline as predicting hit with probability equal to 'the overall proportion of actual hits in the specific benchmark dataset'. With one pooled rate (485 of 1,175 = 41.3%, Table 3) the expected PPV, sensitivity, F1 and FPR are all about 0.41 and specificity and NPV about 0.59. Table 8 prints PPV 0.47, sensitivity 0.46, F1 0.47, FPR 0.36, specificity 0.64 and NPV 0.63. With each screen's own hit rate (Table 3) the expected values are 0.47, 0.47, 0.47, 0.37, 0.63 and 0.63, which match. One draw at the pooled rate would need sensitivity about 2 standard errors above and FPR about 3 below expectation at once. The baseline therefore probably uses per-screen hit rates, which is information a screen designer would not have before the screen. The expected values were computed by the reviewer from Table 3 counts and are not stored as results. Treat the random row as a per-screen-prevalence classifier, not a matched random-selection comparator.Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Appendix A.2; Table 3; Table 1, row "Random Baseline"; Table 8, row "Random N/A N/A" Methods, coverage and source GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark pheno-20261022-protocol-song2025-simple-hit-classification Aggregation: Not reported Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Table 8, row "GPT-4o 1-shot (+) N", column "Specificity"

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Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? · Original source · Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30

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Technical metadata and extraction receipts

Stable ID: pheno-20261022-config-song2025-gpt-4o-1-shot-pos-cot-n

areas: cells-tissues
contexts: research
method types: foundation_model
reported name: GPT-4o
configuration type: paper_evaluation
foundation model eligible: true
version: gpt-4o-2024-11-20
model version: gpt-4o-2024-11-20
parameters: Few-shot: 1-shot (+); CoT: N. (+)/(-) mark positive/negative examples; for 2-shot the order is given
temperature note: Temperature 0 with a fixed random seed
access reuse: OpenAI API
source locator: Table 8, row "GPT-4o 1-shot (+) N"
provenance note: Prompted by Song et al. 2025; the model is third-party.

Related records

configuration of: GPT-4o
system: GPT-4o, 1-shot (+), CoT N, on the Virtual CRISPR Simple Benchmark

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