Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers
This addresses the problem of slow and complex LLM-based ranking for information retrieval tasks, offering a more practical solution, though it is incremental as it builds on existing pairwise methods.
The paper tackles the inefficiency and prompt engineering reliance of pairwise/listwise LLM ranking by introducing instruction distillation to convert pairwise ranking into efficient pointwise ranking, improving efficiency by 10-100x and achieving performance comparable to state-of-the-art zero-shot methods.
Recent studies have demonstrated the great potential of Large Language Models (LLMs) serving as zero-shot relevance rankers. The typical approach involves making comparisons between pairs or lists of documents. Although effective, these listwise and pairwise methods are not efficient and also heavily rely on intricate prompt engineering. To tackle this problem, we introduce a novel instruction distillation method. The key idea is to distill the pairwise ranking ability of open-sourced LLMs to a simpler but more efficient pointwise ranking. Specifically, given the same LLM, we first rank documents using the effective pairwise approach with complex instructions, and then distill the teacher predictions to the pointwise approach with simpler instructions. Evaluation results on the BEIR, TREC, and ReDial datasets demonstrate that instruction distillation can improve efficiency by 10 to 100x and also enhance the ranking performance of LLMs. Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods. The code to reproduce our results is available at www.github.com/sunnweiwei/RankGPT.