Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom.
Table of Links
Storytelling, The Shape of Stories, and Log Lines
The Use of Large Language Models for Creative Text Generation
Evaluating Text Generated by Large Language Models
Conclusions, Acknowledgements, and References
A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION
B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM
C. DETAILS OF QUANTITATIVE OBSERVATIONS
E. FULL PROMPT PREFIXES FOR DRAMATRON
F. RAW OUTPUT GENERATED BY DRAMATRON
B ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM
Discussions amongst the creative team were summarized in the body of the text (see Section 5.9).
To reiterate, four key themes emerged through these discussions which echo the themes presented in Section 5. These themes are discussed in detail in this section, alongside supporting quotes.
First, the system has a distinct glitch style that can sometimes be nonsensical, vague, or passive. As one performer recounted, “sometimes there is internal conflict within the text, and in those moments it is as if [Dramatron] is working against itself”. This pushes performers to commit to choices, and to be concise and specific and complimentary in their interpretation. One performer noted that if the system generated flawless text, “it might not work as well”, because “the mistakes in the script are the joy”. Another went a step further, saying “I almost want more curveballs and non-sequiturs... more crunchy bits”. Overall, the sentiment of enjoying the style of the system was a common theme, with several of the performers remarking that “some of the funniest parts are when you can tell a robot made it”, and that the audience “wants to hear the robot’s voice”.
Secondly, the generated text can sometimes be repetitive. This can be interpreted as a mistake. Or, this repetition can be fun and playful if it is interpreted as an important and deliberate choice from the playwright: “a choice to be honored,” as one performer said. When the system did repeat itself, the cast was able to make the lines more meaningful with their own unique human talents. As one said, “you can do so much with your line reading, with your physicality, delivery, proximity, and acting choices.”
Third, the team discussed agency and expectations of the systems capabilities. For instance, in the way that the performers would refer to the system’s choices. One said “Dramatron was trying to show me the angle,” or “I was trying to understand what Dramatron meant”. Discussions amongst the creative team explored their expectations of what the system could and could not do. For example, “it is just a bot, not really fully understanding how [the world] works”, and another responded “Dramatron is trying.”
Finally, the prevailing feedback from the majority of performers was that participating in the production was fun. As one actor said, “it was liberating and easy because the world creating was done for you, the platform was done, and that can be mentally exhausting as an improviser.” These reflections discussed by the creative team reflect the usefulness of co-written scripts. This is particularly true when used for a production such as Plays By Bots, which leverages professional improvisers to interpret the scripts. The co-creativity of a system such as Dramatron extends beyond the playwright and Dramatron, and to the performer on the stage working with the generated and edited text.
These reflections discussed by the creative team reflect the usefulness of co-written scripts.
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