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[2602.23329] LLM Novice Uplift on Dual-Use, In Silico Biology Tasks
Summary
This article examines the effectiveness of large language models (LLMs) in enhancing novice users' performance on complex biological tasks, revealing significant accuracy improvements over traditional internet resources.
Why It Matters
Understanding how LLMs can uplift novice users in specialized fields like biology is crucial for both scientific advancement and addressing dual-use risks. This research highlights the potential of LLMs to democratize access to advanced knowledge, while also raising questions about their responsible use.
Key Takeaways
- LLM access significantly improves novice accuracy on biological tasks.
- Novices using LLMs outperformed experts in three out of four benchmarks.
- Standalone LLMs often provided better results than LLM-assisted novices.
- Most participants found it easy to access dual-use information despite safeguards.
- The study emphasizes the need for ongoing evaluations of LLM effectiveness.
Computer Science > Artificial Intelligence arXiv:2602.23329 (cs) [Submitted on 26 Feb 2026] Title:LLM Novice Uplift on Dual-Use, In Silico Biology Tasks Authors:Chen Bo Calvin Zhang, Christina Q. Knight, Nicholas Kruus, Jason Hausenloy, Pedro Medeiros, Nathaniel Li, Aiden Kim, Yury Orlovskiy, Coleman Breen, Bryce Cai, Jasper Götting, Andrew Bo Liu, Samira Nedungadi, Paula Rodriguez, Yannis Yiming He, Mohamed Shaaban, Zifan Wang, Seth Donoughe, Julian Michael View a PDF of the paper titled LLM Novice Uplift on Dual-Use, In Silico Biology Tasks, by Chen Bo Calvin Zhang and 18 other authors View PDF HTML (experimental) 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, sta...
