Records found and checked by machines, not reviewed by an editor · What this means →

Document · Research paper (work)

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Weak-model supervision experiments presented as an analogy for supervising superhuman models.

Issued by
Authors credited
No authorship claim recorded yet. The source record lists Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever and Jeff Wu.
Dates
  • Published 2023-12-14
Exact source

Appears in

No collection holds a record about this document yet.

Versions

No other versions of this document are recorded. The evidence applies to the source version listed above. Other versions, such as a preprint, working paper or journal publication, are not identified or substituted.

Issue and authorship records

  • OpenAI: institution →

Documentary relations

Each relation is listed separately. Citing, crediting and reporting the use of a method are different acts, and none of them shows agreement or influence by itself.

Cites · 4

Explicit references in this document. Citation is neither agreement nor influence.

  • Supervising strong learners by amplifying weak experts: document page →
  • Self-critiquing models for assisting human evaluators: document page →
  • AI safety via debate: document page →
  • Scalable agent alignment via reward modeling: a research direction: document page →

Discusses · 1

Concepts explicitly discussed. No earliest use, invention or transmission is claimed.

Not recorded for this document: cited by · explicitly credits · reports using a method from · method reported as used by · applies parts of · parts applied by · reports evaluation of. A missing record does not show that the relation does not exist.