Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in
  Automatic Related Work Composition
Anna Martin-Boyle Aahan Tyagi Marti A. Hearst Dongyeop Kang
Numerous AI-assisted scholarly applications have been developed to aid
different stages of the research process. We present an analysis of AI-assisted
scholarly writing generated with ScholaCite, a tool we built that is designed
for organizing literature and composing Related Work sections for academic
papers. Our evaluation method focuses on the analysis of citation graphs to
assess the structural complexity and inter-connectedness of citations in texts
and involves a three-way comparison between (1) original human-written texts,
(2) purely GPT-generated texts, and (3) human-AI collaborative texts. We find
that GPT-4 can generate reasonable coarse-grained citation groupings to support
human users in brainstorming, but fails to perform detailed synthesis of
related works without human intervention. We suggest that future writing
assistant tools should not be used to draft text independently of the human
author.