How should the advent of large language models affect the practice of
  science?
Marcel Binz Stephan Alaniz Adina Roskies Balazs Aczel Carl T. Bergstrom Colin Allen Daniel Schad Dirk Wulff Jevin D. West Qiong Zhang Richard M. Shiffrin Samuel J. Gershman Ven Popov Emily M. Bender Marco Marelli Matthew M. Botvinick Zeynep Akata Eric Schulz
Large language models (LLMs) are being increasingly incorporated into
scientific workflows. However, we have yet to fully grasp the implications of
this integration. How should the advent of large language models affect the
practice of science? For this opinion piece, we have invited four diverse
groups of scientists to reflect on this query, sharing their perspectives and
engaging in debate. Schulz et al. make the argument that working with LLMs is
not fundamentally different from working with human collaborators, while Bender
et al. argue that LLMs are often misused and over-hyped, and that their
limitations warrant a focus on more specialized, easily interpretable tools.
Marelli et al. emphasize the importance of transparent attribution and
responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans
should retain responsibility for determining the scientific roadmap. To
facilitate the discussion, the four perspectives are complemented with a
response from each group. By putting these different perspectives in
conversation, we aim to bring attention to important considerations within the
academic community regarding the adoption of LLMs and their impact on both
current and future scientific practices.