Pith. sign in

REVIEW 4 cited by

Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2209.14958 v1 pith:ROTKLRYA submitted 2022-09-29 cs.HC cs.CL

Piotr Mirowski , Kory W. Mathewson , Jaylen Pittman , Richard Evans This is my paper
classification cs.HCcs.CL
keywords dramatronmodelslanguagescreenplaysscriptstheatreco-creativityillustrate
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Language models are increasingly attracting interest from writers. However, such models lack long-range semantic coherence, limiting their usefulness for longform creative writing. We address this limitation by applying language models hierarchically, in a system we call Dramatron. By building structural context via prompt chaining, Dramatron can generate coherent scripts and screenplays complete with title, characters, story beats, location descriptions, and dialogue. We illustrate Dramatron's usefulness as an interactive co-creative system with a user study of 15 theatre and film industry professionals. Participants co-wrote theatre scripts and screenplays with Dramatron and engaged in open-ended interviews. We report critical reflections both from our interviewees and from independent reviewers who watched stagings of the works to illustrate how both Dramatron and hierarchical text generation could be useful for human-machine co-creativity. Finally, we discuss the suitability of Dramatron for co-creativity, ethical considerations -- including plagiarism and bias -- and participatory models for the design and deployment of such tools.

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Training Skills Like Parameters via Self-Supervised Semantic Diffusion

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Screenwriting skills can be learned from human scripts alone by compressing and reconstructing them, storing the lessons as text cards that improve generation without changing model weights.

  2. DramaDirector: Geometry-Guided Short Drama Generation

    cs.CV 2026-06 conditional novelty 6.0 of 10

    Geometry-indexed depth–pose retrieval plus schema SFT and GRPO planning improves faithfulness, consistency, and controllability of plot-to-short-drama video generation over multi-agent and text-only baselines.

  3. NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    NarrativeWorldBench evaluates 21 LLMs on nine narrative metrics across horizons to 200 episodes and introduces N-VSSM, a 256-dimensional variational state-space model that achieves plot-beat F1 >=0.84 with 4x lower co...

  4. DramaDirector: Geometry-Guided Short Drama Generation

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    DramaDirector retrieves depth-pose references from real drama shots to guide first-frame and image-to-video synthesis for plot-driven short dramas, paired with the DramaBoard benchmark.

Pith tools