FilterPilot: An Interactive Assistant for Adapting Filtering Predicate to Table Content
TL;DR
FilterPilot, an LLM-powered interactive assistant designed to adapt filtering predicates to table content, employs a novel iterative recall-then-verify paradigm, combining LLM-based query reformulation with table-value feedback to dynamically expand search terms.
Abstract
Formulating effective filtering conditions for database queries often requires a deep understanding of the specific values stored within table columns, which can impose a significant cognitive burden on users, especially when the textual columns contain hundreds or even thousands of unique values. To address this challenge, we introduce FilterPilot, an LLM-powered interactive assistant designed to adapt filtering predicates to table content. Given user-provided filtering terms and optional time constraints, FilterPilot assists by suggesting appropriate filtering conditions. At its core, FilterPilot employs a novel iterative recall-then-verify paradigm, combining LLM-based query reformulation with table-value feedback to dynamically expand search terms. This process is further optimized through an Upper Confidence Bound (UCB) selection mechanism and an early stopping strategy. We release the source code, datasets, and a live demo system on https://github.com/wusw14/FilterPilot to encourage community adoption.