arXiv subjects

Brett A. Halperin

Publications and source records attributed to Brett A. Halperin.

4 recordsLinked to original sources

arXiv2026

Elsewise: Authoring Open-ended Interactive Narrative with Possibility Space Visualization

Interactive narrative (IN) authors craft spaces of divergent narrative possibilities for players to explore, with the player's input determining which narrative possibilities they actually experience. Generative AI can enable new forms of IN by improvisationally expanding on pre-authored content in response to open-ended player input. However, this extrapolation risks widening the gap between author-envisioned and player-experienced stories, potentially limiting the strength of plot progression and the communication of the author's narrative intent. To bridge the gap, we introduce Elsewise: an authoring tool for LLM-based INs that implements a novel Bundled Storyline concept to enhance author's perception and understanding of the narrative possibility space, allowing authors to explore similarities and differences between possible playthroughs of their IN in terms of open-ended, user-configurable narrative dimensions. A user study (n=12) shows that our approach improves author anticipation of player-experienced narrative, leading to more effective control and exploration of the narrative possibility spaces.

cs.HC↗

arXiv2025

LiteraryTaste: A Preference Dataset for Creative Writing Personalization

People have different creative writing preferences, and large language models (LLMs) for these tasks can benefit from adapting to each user's preferences. However, these models are often trained over a dataset that considers varying personal tastes as a monolith. To facilitate developing personalized creative writing LLMs, we introduce LiteraryTaste, a dataset of reading preferences from 60 people, where each person: 1) self-reported their reading habits and tastes (stated preference), and 2) annotated their preferences over 100 pairs of short creative writing texts (revealed preference). With our dataset, we found that: 1) people diverge on creative writing preferences, 2) finetuning a transformer encoder could achieve 75.8% and 67.7% accuracy when modeling personal and collective revealed preferences, and 3) stated preferences had limited utility in modeling revealed preferences. With an LLM-driven interpretability pipeline, we analyzed how people's preferences vary. We hope our work serves as a cornerstone for personalizing creative writing technologies.

cs.CL↗

arXiv2025

When Discourse Stalls: Moving Past Five Semantic Stopsigns about Generative AI in Design Research

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns" -- reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies.

cs.CY↗

arXiv2023

Envisioning Narrative Intelligence: A Creative Visual Storytelling Anthology

In this paper, we collect an anthology of 100 visual stories from authors who participated in our systematic creative process of improvised story-building based on image sequences. Following close reading and thematic analysis of our anthology, we present five themes that characterize the variations found in this creative visual storytelling process: (1) Narrating What is in Vision vs. Envisioning; (2) Dynamically Characterizing Entities/Objects; (3) Sensing Experiential Information About the Scenery; (4) Modulating the Mood; (5) Encoding Narrative Biases. In understanding the varied ways that people derive stories from images, we offer considerations for collecting story-driven training data to inform automatic story generation. In correspondence with each theme, we envision narrative intelligence criteria for computational visual storytelling as: creative, reliable, expressive, grounded, and responsible. From these criteria, we discuss how to foreground creative expression, account for biases, and operate in the bounds of visual storyworlds.

cs.CL↗