  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.
