Submission Type: Regular Short Paper
Submission Track: Information Retrieval and Text Mining
Submission Track 2: Theme Track: Large Language Models and the Future of NLP
Keywords: large language model, query likelihood model, zero-shot ranking model
Abstract: 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.
Submission Number: 1524
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