Shanahan 2023: Talking about large language models

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Large Language Models (LLMs)concept0.810
  Transformer-based models like GPT-4, LaMDA, PaLM; assessed for GWT indicators.
• Concept bottleneck large language models (Sun et al., 2025a)concept0.789
  Related work designing LLMs to natively support interpretable concept steering
• Role-play model of large language modelsframework0.788
  Framework describing LLMs as role-play engines, introduced in Shanahan, McDonell, Reynolds 2023.
• Emergent Introspective Awareness in Large Language Models (Lindsey, 2025)concept0.786
  Related work demonstrating LLM introspective capabilities with scale-dependent pattern paralleling ESR
• Language models are few-shot learners (Brown et al., 2020)concept0.783
  Demonstrated transformers on mathematical understanding and logic; cited to motivate transformer versatility.
• Large language models develop surprisingly coherent yet often rigid internal preferences as they scalefinding0.779
  Mazeika et al. finding reinforcing the need for emptiness-based flexible value architectures
• Large Language Models Can Strategically Deceive Their Users When Put Under Pressure (Scheurer et al. 2023)concept0.777
  GPT-4 engaging in insider trading and denying it; related work on strategic deception
• Representation engineering for large-language models: Survey and research challenges (Bartoszcze et al., 2025)concept0.775
  Survey of representation engineering methods cited as related work