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.

翻译：随着大语言模型（LLM）的兴起和普及，使用自然语言进行查询、对话以及控制搜索和信息检索界面正迅速成为常态。在这篇立场论文中，我们描述了一个使用LLM进行交互式查询重写的通用框架。我们的提议旨在揭示在利用LLM构建高性能检索系统的同时，实现更优且更透明的意图理解的新机遇。该框架的一个关键方面是，重写器能够通过搜索引擎用自然语言完整地指定机器意图，并且在最终检索阶段之前，该意图可以被进一步细化、控制和编辑。以自然语言呈现、交互和推理底层机器意图的能力，对透明度、排序性能以及传统上用于理解意图的监督信号收集方式产生了深远影响。我们详细阐述了该概念，并辅以初步实验，同时提出了这一交互式查询理解框架中尚待解决的开放性问题。