A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
This work addresses efficiency bottlenecks in LLM-based ranking for information retrieval, offering a novel method that balances performance and computational cost.
The paper tackles the trade-off between effectiveness and efficiency in zero-shot document ranking with Large Language Models by proposing a Setwise prompting approach, which reduces LLM inferences and token consumption while maintaining high ranking effectiveness.
We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at \url{https://github.com/ielab/llm-rankers}.