  In the field of information retrieval, Query Likelihood Models (QLMs) rank
documents based on the probability of generating the query given the content of
a document. Recently, advanced large language models (LLMs) have emerged as
effective QLMs, showcasing promising ranking capabilities. This paper focuses
on investigating the genuine zero-shot ranking effectiveness of recent LLMs,
which are solely pre-trained on unstructured text data without supervised
instruction fine-tuning. Our findings reveal the robust zero-shot ranking
ability of such LLMs, highlighting that additional instruction fine-tuning may
hinder effectiveness unless a question generation task is present in the
fine-tuning dataset. Furthermore, we introduce a novel state-of-the-art ranking
system that integrates LLM-based QLMs with a hybrid zero-shot retriever,
demonstrating exceptional effectiveness in both zero-shot and few-shot
scenarios. We make our codebase publicly available at
https://github.com/ielab/llm-qlm.
