Rank-without-GPT: Building GPT-Independent Listwise Rerankers on
  Open-Source Large Language Models
Xinyu Zhang Sebastian Hofstätter Patrick Lewis Raphael Tang Jimmy Lin
Listwise rerankers based on large language models (LLM) are the zero-shot
state-of-the-art. However, current works in this direction all depend on the
GPT models, making it a single point of failure in scientific reproducibility.
Moreover, it raises the concern that the current research findings only hold
for GPT models but not LLM in general. In this work, we lift this pre-condition
and build for the first time effective listwise rerankers without any form of
dependency on GPT. Our passage retrieval experiments show that our best list se
reranker surpasses the listwise rerankers based on GPT-3.5 by 13% and achieves
97% effectiveness of the ones built on GPT-4. Our results also show that the
existing training datasets, which were expressly constructed for pointwise
ranking, are insufficient for building such listwise rerankers. Instead,
high-quality listwise ranking data is required and crucial, calling for further
work on building human-annotated listwise data resources.