LLM Novice Uplift on Dual-Use, In Silico Biology Tasks

• URL: http://arxiv.org/abs/2602.23329v1
• Date: Thu, 26 Feb 2026 18:37:23 GMT
• Title: LLM Novice Uplift on Dual-Use, In Silico Biology Tasks
• Abstract summary: Large language models (LLMs) perform increasingly well on biology benchmarks.<n>It remains unclear whether they uplift novice users.<n>This uncertainty is central to understanding both scientific acceleration and dual-use risk.
• Score: 6.174411715393828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable humans to perform better than with internet-only resources. This uncertainty is central to understanding both scientific acceleration and dual-use risk. We conducted a multi-model, multi-benchmark human uplift study comparing novices with LLM access versus internet-only access across eight biosecurity-relevant task sets. Participants worked on complex problems with ample time (up to 13 hours for the most involved tasks). We found that LLM access provided substantial uplift: novices with LLMs were 4.16 times more accurate than controls (95% CI [2.63, 6.87]). On four benchmarks with available expert baselines (internet-only), novices with LLMs outperformed experts on three of them. Perhaps surprisingly, standalone LLMs often exceeded LLM-assisted novices, indicating that users were not eliciting the strongest available contributions from the LLMs. Most participants (89.6%) reported little difficulty obtaining dual-use-relevant information despite safeguards. Overall, LLMs substantially uplift novices on biological tasks previously reserved for trained practitioners, underscoring the need for sustained, interactive uplift evaluations alongside traditional benchmarks.

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