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
Published: 2026-02-26

Source: https://arxiv.org/abs/2602.23329

PDF: https://arxiv.org/pdf/2602.23329

AIにより推定されたラベル

LLM性能評価 教育データマイニング モデル評価手法

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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