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

2026-02-26

AI Summary

This study systematically evaluates whether large language models (LLMs) can substantially enhance the performance of biological novices in high-stakes dual-use computational biology tasks. Through multi-model, multi-benchmark human experiments, the authors compare novices’ accuracy on eight biosafety-related tasks with and without LLM assistance, benchmarking against both internet-assisted experts and standalone model performance. Results demonstrate that LLM support increases novices’ accuracy by a factor of 4.16 relative to unassisted controls and even surpasses internet-assisted experts on three tasks. Notably, 89.6% of participants readily accessed sensitive dual-use information, highlighting both the transformative potential and significant safety risks of human-LLM collaboration. This work provides the first quantitative assessment of LLMs’ real-world augmentation effect for non-experts in high-risk biological contexts.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• large language models
• novice uplift
• dual-use
• in silico biology
• biosecurity

Innovation

• human uplift
• dual-use risk
• large language models
• in silico biology
• novice performance