Model

GPT-4 (gpt-4-0125-preview)

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"

OpenAI GPT-4 large language model at the gpt-4-0125-preview API snapshot.

Results are recorded on linked configurations and versions of this model. Their settings and evaluations are kept separate below.

1 configuration with results

Results on linked records

This model has no results of its own in this release. One linked record has results. They are not assigned to the underlying model: each record keeps its own settings and evaluations, and its scores are on its own page.

GPT-4, direct prompting, mean over prompting strategies (Song et al. 2025) Configuration · configuration of this model · 1 evaluation · 2 results

Overview

An explanatory profile has not yet been reviewed for this record.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Use this model

How it works, versions and access

Additional methodological details, versions and access terms have not yet been extracted into a structured profile for this model. Consult the linked sources and evaluation records below.

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

0 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

No evidence rows match these filters. Choose another scope or clear the search.

How to read the evidence

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

Download this release (gzip)

Technical metadata and extraction receipts

Stable ID: pheno-20261022-model-song2025-gpt-4

areas: cells-tissues
contexts: research
method types: foundation_model
entity level: service
reported name: GPT-4
version: gpt-4-0125-preview
access: Commercial API (OpenAI)
source locator: Appendix Table 7; Appendix A.1
provenance note: Version string as printed in Appendix Table 7 of Song et al. 2025. The description is general knowledge of the model line, not taken from the source.
missing metadata: training data: reason: unreported

Related records

configuration of: GPT-4, direct prompting, mean over prompting strategies (Song et al. 2025)

Suggest a correction