{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/llms-as-method-actors-a-model-for-prompt","title":"LLMs as Method Actors: A Model for Prompt Engineering and Architecture","arxiv_id":"2411.05778","date":"2024-11-08","proceeding":null,"authors":["Colin Doyle"],"abstract":"We introduce \"Method Actors\" as a mental model for guiding LLM prompt engineering and prompt architecture. Under this mental model, LLMs should be thought of as actors; prompts as scripts and cues; and LLM responses as performances. We apply this mental model to the task of improving LLM performance at playing Connections, a New York Times word puzzle game that prior research identified as a challenging benchmark for evaluating LLM reasoning. Our experiments with GPT-4o show that a \"Method Actors\" approach can significantly improve LLM performance over both a vanilla and \"Chain of Thoughts\" approach. A vanilla approach solves 27% of Connections puzzles in our dataset and a \"Chain of Thoughts\" approach solves 41% of puzzles, whereas our strongest \"Method Actor\" approach solves 86% of puzzles. We also test OpenAI's newest model designed specifically for complex reasoning tasks, o1-preview. When asked to solve a puzzle all at once, o1-preview solves 79% of Connections puzzles in our dataset, and when allowed to build puzzle solutions one guess at a time over multiple API calls, o1-preview solves 100% of the puzzles. Incorporating a \"Method Actor\" prompt architecture increases the percentage of puzzles that o1-preview solves perfectly from 76% to 87%.","url_abs":"https://arxiv.org/abs/2411.05778v2","url_pdf":"https://arxiv.org/pdf/2411.05778v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"llms-as-method-actors-a-model-for-prompt","repo_url":"https://github.com/colindoyle0000/llms-as-method-actors","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prompt-engineering","task_name":"Prompt Engineering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}