Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers (2311.01555v1)

Abstract: Recent studies have demonstrated the great potential of 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.

Instruction Distillation for Efficient Zero-Shot Ranking with LLMs

The paper, "Instruction Distillation Makes LLMs Efficient Zero-shot Rankers," addresses the inefficiencies inherent in using LLMs for reranking tasks in Information Retrieval (IR). Traditional methods leveraging LLMs often rely on complex pairwise and listwise ranking strategies that require intricate prompt engineering and entail significant computational costs. The authors propose an innovative methodology termed "Instruction Distillation" to improve the efficiency and effectiveness of LLM-based ranking tasks.

Overview of the Approach

The central contribution of the paper is the instruction distillation method, which seeks to transfer the ranking capabilities of computationally intensive pairwise approaches to a more efficient pointwise system. This is achieved by using a teacher-student framework where predictions from a teacher model, generated using pairwise prompting, are distilled into a simpler, student model employing pointwise prompting. This transformation not only enhances efficiency but also stabilizes the output, making it suitable for practical applications.

Empirical Evaluation

The empirical evaluation of the proposed method is conducted on several datasets, including BEIR, TREC-DL, and ReDial. The results indicate a significant improvement in efficiency, with the distilled models being 10 to 100 times faster than their teacher counterparts. Despite this increase in speed, the distilled models also demonstrate enhanced ranking performance, surpassing state-of-the-art supervised methods like monoT5 and aligning closely with leading zero-shot methods.

The results show that the instruction-distilled model based on FLAN-T5-XL matches or even surpasses the monoT5-3B system, achieving improved nDCG scores across the tested datasets. This efficiency gain, coupled with performance improvements, marks a significant step forward in making LLMs applicable for real-world IR tasks.

Methodological Insights

The paper outlines a robust methodological framework for instruction distillation. The process begins with candidate generation, followed by teacher model inference using pairwise ranking methods, and culminates in the optimization of the student model using RankNet loss. This sequence ensures that the student model retains and even enhances the distilled knowledge from its teacher, allowing for efficient pointwise scoring that maintains high accuracy.

Implications and Future Directions

The implications of this research are multifaceted, impacting both theoretical and practical domains of AI and IR. Theoretically, it opens new avenues for simplifying complex LLM-based tasks through innovative instruction strategies. Practically, it offers a feasible pathway for deploying LLMs in computationally constrained environments, such as mobile applications or edge devices, where efficiency is critical.

Looking ahead, the approach could be extended to other NLP tasks beyond IR, potentially transforming how complex NLP models are fine-tuned and deployed in resource-limited scenarios. Further research could explore the integration of this distillation technique with other model architectures or investigate its applicability in multilingual contexts, expanding its utility across broader domains.

In conclusion, the instruction distillation approach effectively bridges the gap between efficiency and performance in LLM-based ranking tasks, offering a compelling solution to the challenges posed by existing zero-shot ranking methods. This research represents a substantive contribution to both the fields of IR and NLP, setting the stage for continued advancements in efficient model deployment.