mlsanityData to 14 Oct 2026

Guided scenarios with simulated expert personae: a remarkable strategy to perform cognitive work

arXiv 2306.03104 ↗First posted 3 Jun 2023cs.HC cs.AI quant-phcited by 3

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Abstract (arXiv)

Large language models (LLMs) trained on a substantial corpus of human knowledge and literature productively work with a large array of facts from that corpus. Surprisingly, they are also able to re-create the behaviors of personae that are captured within the corpus. By forming teams of simulated personae, supplying contexts that set the stage, and providing gentle prompts, one can move through scenarios that elicit expert behavior to perform meaningful cognitive work. The power of this strategy is demonstrated with two examples, one attacking factuality of LLM responses and the other reproducing a very recently published result in quantum optics.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 3 Jun 2023.

Builds on

7 of 12 references.

References · 7

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  4. 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biasescited by 155
  5. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  6. 2023ExpertPrompting: Instructing Large Language Models to be Distinguished Expertscited by 142
  7. 2023Bits of Grass: Does GPT already know how to write like Whitman?cited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.