Toward a Better Evaluations Ecosystem

·LessWrong··

Model evaluations are broken. Numbers that are often cited alongside one another as evidence of progress are rarely comparable due to inconsistent methodologies, and AI companies run and report internal evals that are unavailable to the wider community. But we can fix this.We are making deployment and safety decisions based on numbers that do not mean what people think they mean. Every other high-stakes industry has solved this the same way, by taking the measurements out of the hands of the com...

Read full article →

Related Articles

Training Model to Predict Its Own Generalization: A Preliminary Study
Tianyi (Alex) Qiu · LessWrong · 3d ago
A Theoretical Game of Attacks via Compositional Skills
Xinbo Wu, Huan Zhang, Abhishek Umrawal, Lav R. Varshney · ArXiv cs.CL · 3d ago
BioVeil MATRIX: Uncovering and categorizing vulnerabilities of agentic biological AI scientists
Kimon Antonios Provatas, Avery Self, Ioannis Mouratidis, Ilias Georgakopoulos-Soares · ArXiv q-bio · 3d ago
Irretrievability; or, Murphy's Curse of Oneshotness upon ASI
Eliezer Yudkowsky · LessWrong · 4d ago
Verbalized Eval Awareness Inflates Measured Safety
Santiago Aranguri · LessWrong · 4d ago