Query Understanding in the Age of Large Language Models

TL;DR

This paper proposes a flexible framework for interactive query rewriting using large language models, enhancing transparency and intent understanding in search systems through natural language specification and refinement.

Contribution

It introduces a novel framework for query rewriting with LLMs that allows natural language specification, refinement, and control of search intent, improving transparency and retrieval performance.

Findings

Initial experiments demonstrate the feasibility of the framework.

The approach enhances transparency in query understanding.

Open questions for future research are discussed.

Abstract

Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language models (LLM). In this position paper, we describe a generic framework for interactive query-rewriting using LLMs. Our proposal aims to unfold new opportunities for improved and transparent intent understanding while building high-performance retrieval systems using LLMs. A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language that can be further refined, controlled, and edited before the final retrieval phase. The ability to present, interact, and reason over the underlying machine intent in natural language has profound implications on transparency, ranking performance, and a departure from the traditional way in which supervised signals were collected for understanding intents. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.