CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities

Stanford University · Cornell University

Indexed inarxivcrossref

Abstract

Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs’ generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3’s capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions. We demonstrate that CoAuthor can…

Citation impact

317
total citations
FWCI
29.49
Percentile
100%
References
35
Citations per year

Authors

3

Topics & keywords

Keywords
  • Computer science
  • GRASP
  • Generative grammar
  • Context (archaeology)
  • Argumentative
  • Human–computer interaction
  • Ideation
  • Interface (matter)
UN Sustainable Development Goals
  • Quality Education
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