QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation

TL;DR

This paper introduces QUILL, a method that enhances query understanding in large language models by combining retrieval augmentation with a novel two-stage distillation process, leading to significant real-world performance improvements.

Contribution

The paper presents a practical two-stage distillation technique that preserves retrieval augmentation benefits in LLMs without increased computational costs.

Findings

Improved query understanding with retrieval augmentation.

Effective distillation retains retrieval benefits.

Significant gains on real-world large-scale query systems.

Abstract

Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lack of nuance or context. Complicated feature engineering efforts do not always lead to downstream improvements as their performance benefits may be offset by increased complexity of knowledge distillation. Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding. While Retrieval Augmentation typically increases latency of LMs (thus hurting distillation efficacy), (2) we provide a practical and effective way of distilling Retrieval Augmentation LLMs. Specifically, we use a novel two-stage distillation approach that allows us to carry over the gains of retrieval augmentation, without suffering the increased compute typically associated with it. (3) We demonstrate the benefits of the proposed approach (QUILL) on a billion-scale, real-world query understanding system resulting in huge gains. Via extensive experiments, including on public benchmarks, we believe this work offers a recipe for practical use of retrieval-augmented query understanding.