framework
active
framework:role-play-model-of-large-language-modelsRole-play model of large language models
Framework describing LLMs as role-play engines, introduced in Shanahan, McDonell, Reynolds 2023.
Neighborhood — ranked by edge-count
Methods (1)
method
- Role-play prompting techniqueimplementsMethod of eliciting specific personas from an LLM through prompt design.
Artifacts (2)
artifact
- The primary paper under study; presents a case study of an AI-generated Buddhist sutra and philosophical analysis.
- Paper introducing the role-play model of LLMs; cited in §3.1.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Transformer-based models like GPT-4, LaMDA, PaLM; assessed for GWT indicators.
- Primary test domain for manifold steering, including reasoning and ICL tasks
- Primary substrate for manifold steering experiments; demonstrates method on reasoning and in-context tasks.
- Prior paper by Shanahan cautioning against anthropomorphic terms for LLMs; cited as ref 1
- Demonstrated transformers on mathematical understanding and logic; cited to motivate transformer versatility.
- Survey of representation engineering methods cited as related work
- The primary conceptual framework proposed: understanding dialogue agent behaviour as role play of characters
- Paper hypothesising LLMs model agent beliefs/desires/intentions with preliminary GPT-3 evidence; cited as ref 2