The rise of unifying frameworks that enable seamless interoperability of
Large Language Models (LLMs) has made LLM-LLM collaboration for open-ended
tasks a possibility. Despite this, there have not been efforts to explore such
collaborative writing. We take the next step beyond human-LLM collaboration to
explore this multi-LLM scenario by generating the first exclusively
LLM-generated collaborative stories dataset called CollabStory. We focus on
single-author ($N=1$) to multi-author (up to $N=5$) scenarios, where multiple
LLMs co-author stories. We generate over 32k stories using open-source
instruction-tuned LLMs. Further, we take inspiration from the PAN tasks that
have set the standard for human-human multi-author writing tasks and analysis.
We extend their authorship-related tasks for multi-LLM settings and present
baselines for LLM-LLM collaboration. We find that current baselines are not
able to handle this emerging scenario. Thus, CollabStory is a resource that
could help propel an understanding as well as the development of techniques to
discern the use of multiple LLMs. This is crucial to study in the context of
writing tasks since LLM-LLM collaboration could potentially overwhelm ongoing
challenges related to plagiarism detection, credit assignment, maintaining
academic integrity in educational settings, and addressing copyright
infringement concerns. We make our dataset and code available at
\texttt{https://github.com/saranya-venkatraman/multi_llm_story_writing}.

LLM 模型间的合作创作及其影响是一个重要的研究方向，本研究生成了 CollabStory 数据集，用于探索单一作者与多作者间合作创作的情景，并提出了解决多 LLM 模型合作创作的基线方法。