15 citations · 45 across the 6 of their papers we have counts for
6 papers
HCAST: Human-Calibrated Autonomy Software Tasks
David Rein, Joel Becker, Amy Deng +19
To understand and predict the societal impacts of highly autonomous AI systems, we need benchmarks with grounding, i.e., metrics that directly connect AI performance to real-world…
Measuring AI Ability to Complete Long Software Tasks
Thomas Kwa, Ben West, Joel Becker +23
Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear. To quantify the capabilities of AI systems in terms of human capabilities,…
RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
Hjalmar Wijk, Tao Lin, Joel Becker +20
Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations fo…
Evaluating Language-Model Agents on Realistic Autonomous Tasks
Megan Kinniment, Lucas Jun Koba Sato, Haoxing Du +10
In this report, we explore the ability of language model agents to acquire resources, create copies of themselves, and adapt to novel challenges they encounter in the wild. We refe…
Language models are better than humans at next-token prediction
Buck Shlegeris, Fabien Roger, Lawrence Chan +1
Current language models are considered to have sub-human capabilities at natural language tasks like question-answering or writing code. However, language models are not trained to…
Adversarial Training for High-Stakes Reliability
Daniel M. Ziegler, Seraphina Nix, Lawrence Chan +9
In the future, powerful AI systems may be deployed in high-stakes settings, where a single failure could be catastrophic. One technique for improving AI safety in high-stakes setti…