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In a 2-hour paid study with 15 playwrights/screenwriters, writers used it mainly for inspiration, character/world alternatives and rough material, not finished scripts; five co-written scripts were staged at the 2022 Edmonton Fringe.","status":"active","body":{"text":"Dramatron (DeepMind, Sept 2022) chains prompts hierarchically from a writer-supplied log line to title, characters, plot (scene beats), location descriptions and per-scene dialogue, using Chinchilla 70B with few-shot prompts; the writer can regenerate, continue or edit at every level. In a 2-hour paid study with 15 playwrights/screenwriters, writers used it mainly for inspiration, character/world alternatives and rough material, not finished scripts; five co-written scripts were staged at the 2022 Edmonton Fringe.\n\nCurator qualifications: 'In this study, we employed the Chinchilla large language model (LLM) [48], represented as a neural network with 70B-parameters and that was trained on 1.4T tokens of the MassiveText dataset'; 'compensate experts for the co-writing and interview sessions at 100 GBP per hour'; 'Fringe' 5 hits.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2209.14958v1","note":"Dramatron arXiv 2209.14958v1 PDF"}]},"categories":["T01","T09","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2209.14958v1","claim":"Dramatron (DeepMind, Sept 2022) chains prompts hierarchically from a writer-supplied log line to title, characters, plot (scene beats), location descriptions and per-scene dialogue, using Chinchilla 70B with few-shot prompts; the writer can regenerate, continue or edit at every level. 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In a 2-hour paid study with 15 playwrights/screenwriters, writers used it mainly for inspiration, character/world alternatives and rough material, not finished scripts; five co-written scripts were staged at the 2022 Edmonton Fringe.\n\nCurator qualifications: 'In this study, we employed the Chinchilla large language model (LLM) [48], represented as a neural network with 70B-parameters and that was trained on 1.4T tokens of the MassiveText dataset'; 'compensate experts for the co-writing and interview sessions at 100 GBP per hour'; 'Fringe' 5 hits.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2209.14958v1","note":"Dramatron arXiv 2209.14958v1 PDF"}]},"tags":[],"type":"article","links":[],"title":"What is Dramatron, how does it generate a script, and what did 15 theatre and film professionals actually use it for?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Dramatron (DeepMind, Sept 2022) chains prompts hierarchically from a writer-supplied log line to title, characters, plot (scene beats), location descriptions and per-scene dialogue, using Chinchilla 70B with few-shot prompts; the writer can regenerate, continue or edit at every level. 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