What is recorded here?
Used language models to generate adversarial test inputs and compared generation and learning strategies for finding undesirable target-model outputs, offering automated discovery as a complement to manual red-teaming.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The search inherits biases from its generator and harm classifier, with trade-offs between diversity and judged severity. Existing supervised and reinforcement-learning methods are components, not all new inventions of this work.
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People and systems
Ethan Perez; Saffron Huang; Francis Song; Trevor Cai; Roman Ring; John Aslanides; Amelia Glaese; Nat McAleese; Geoffrey Irving
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.
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English catalogue narrative
Perez and colleagues: language-model-generated red-team tests
Used language models to generate adversarial test inputs and compared generation and learning strategies for finding undesirable target-model outputs, offering automated discovery as a complement to manual red-teaming.
Scientific scope
The search inherits biases from its generator and harm classifier, with trade-offs between diversity and judged severity. Existing supervised and reinforcement-learning methods are components, not all new inventions of this work.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ethan Perez; Saffron Huang; Francis Song; Trevor Cai; Roman Ring; John Aslanides; Amelia Glaese; Nat McAleese; Geoffrey Irving
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.