Evaluation

GPT-3.5-turbo (direct prompting, mean over strategies) on the Virtual CRISPR Simple Benchmark

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"

Published comparison; transcribed, not reproduced.

Evaluation results

1 evaluation · 2 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)
GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)Evaluation	0.42 ± 0.13	0.39 ± 0.25

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.42 ± 0.13 F1 (Simple Benchmark) fraction · higher Uncertainty: SD 0.13. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed 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-3.5-turbo (direct prompting, mean over strategies) 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 1, row "GPT-3.5-turbo", column "F1 Score"
Configuration: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)	Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit Dataset: Virtual CRISPR Simple Benchmark	0.39 ± 0.25 False positive rate (Simple Benchmark) fraction · lower Uncertainty: SD 0.25. Caption: mean and std. dev. across up to 10 combinations of CoT and few-shot prompting; the number of combinations per model is not printed 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-3.5-turbo (direct prompting, mean over strategies) 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 1, row "GPT-3.5-turbo", column "FPR"

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Evaluation procedure

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

Configuration: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)
Protocol: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit
Dataset: Virtual CRISPR Simple Benchmark

origin: Independent external evaluation
configuration: Primary source as retrieved 2026-10-22
protocol id: pheno-20261022-protocol-song2025-simple-hit-classification
dataset version: BioGRID-ORCS v1.1.16, screens 1837, 1885, 1835, 1733
split: Fixed evaluation set of 1,175 queries; no training on it
population: 1,175 gene-phenotype queries, 41.3% hits
inputs: Natural-language question naming perturbation method, gene, cell line and phenotype
metric implementation: Binary Yes/No answer extracted from generated text; metrics over queries
budget: Not reported
adaptation: Direct prompting, no fine-tuning
aggregation: Mean and standard deviation across up to 10 combinations of chain-of-thought and few-shot prompting strategies

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split: Fixed evaluation set of 1,175 queries; no training on it
Adaptation: Direct prompting, no fine-tuning
Scoring implementation: Binary Yes/No answer extracted from generated text; metrics over queries

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Evidence

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Evidence table

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20 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statement	Original source and location	Review and provenance
Comparison: adaptation Direct prompting, no fine-tuning Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: aggregation Mean and standard deviation across up to 10 combinations of chain-of-thought and few-shot prompting strategies Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: budget Not reported Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	missing or unspecified No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: dataset version BioGRID-ORCS v1.1.16, screens 1837, 1885, 1835, 1733 Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: inputs Natural-language question naming perturbation method, gene, cell line and phenotype Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: metric implementation Binary Yes/No answer extracted from generated text; metrics over queries Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.metric_implementation` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: population 1,175 gene-phenotype queries, 41.3% hits Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.population` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: protocol id pheno-20261022-protocol-song2025-simple-hit-classification Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.protocol_id` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Comparison: split Fixed evaluation set of 1,175 queries; no training on it Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.comparison.split` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact
Limitations 1 values The language model was built by a third party; Song et al. designed the prompts and ran it. Context-only references	Virtual CRISPR: Can LLMs Predict CRISPR Screen Results? Original source ↗ Table 1, row "GPT-3.5-turbo" Version: Proceedings of the 24th Workshop on Biomedical Language Processing (BioNLP 2025), pages 354-364, 1 August 2025; ACL Anthology 2025.bionlp-1.30 Retrieved: 2026-10-22T13:57:26Z	not individually reviewed No individual claim review recorded independent paper Source has a recorded evidence concern. Consult its source page before using the claim. Audit details Field: `attributes.limitations` Source artifact SHA-256: `6c53ffec88444768fbb5c1a65031c096f07b3d9ac48de8163bc8f47d0d51205c` Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/song2025virtualcrispr.py with row labels asserted Inspected artifact

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Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release history

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-eval-song2025-gpt-3-5-turbo-prompting-mean-simple

areas: cells-tissues
contexts: research
origin: independent_paper
protocol: pheno-20261022-protocol-song2025-simple-hit-classification
version: Primary source as retrieved 2026-10-22
comparison: protocol id: pheno-20261022-protocol-song2025-simple-hit-classification; dataset version: BioGRID-ORCS v1.1.16, screens 1837, 1885, 1835, 1733; split: Fixed evaluation set of 1,175 queries; no training on it; population: 1,175 gene-phenotype queries, 41.3% hits; inputs: Natural-language question naming perturbation method, gene, cell line and phenotype; metric implementation: Binary Yes/No answer extracted from generated text; metrics over queries; budget: Not reported; adaptation: Direct prompting, no fine-tuning; aggregation: Mean and standard deviation across up to 10 combinations of chain-of-thought and few-shot prompting strategies
source locator: Table 1, row "GPT-3.5-turbo"
retrospective: true
limitations: The language model was built by a third party; Song et al. designed the prompts and ran it.

Related records

system: GPT-3.5-turbo, direct prompting, mean over prompting strategies (Song et al. 2025)
assessment: Virtual CRISPR Simple Benchmark: yes/no prediction of a screen hit
data: Virtual CRISPR Simple Benchmark
evaluation: song2025-gpt-3-5-turbo-prompting-mean-simple f1-score
evaluation: song2025-gpt-3-5-turbo-prompting-mean-simple false-positive-rate

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