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

Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, Michael Bendersky


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.
Anthology ID:
2022.emnlp-industry.50
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2022
Address:
Abu Dhabi, UAE
Editors:
Yunyao Li, Angeliki Lazaridou
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
492–501
Language:
URL:
https://aclanthology.org/2022.emnlp-industry.50
DOI:
10.18653/v1/2022.emnlp-industry.50
Bibkey:
Cite (ACL):
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, and Michael Bendersky. 2022. QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 492–501, Abu Dhabi, UAE. Association for Computational Linguistics.
Cite (Informal):
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation (Srinivasan et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-industry.50.pdf