# Generating High-Quality Query Suggestion Candidates for Task-Based Search

arXiv: https://arxiv.org/abs/1802.07997 · HTML: https://mlsanity.com/paper/1802.07997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 2 of 2)

- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Query Suggestion for Retrieval-Augmented Generation via Dynamic In-Context Learning](https://mlsanity.com/paper/2601.08105/) (2026)

## Abstract (arXiv)

We address the task of generating query suggestions for task-based search. The current state of the art relies heavily on suggestions provided by a major search engine. In this paper, we solve the task without reliance on search engines. Specifically, we focus on the first step of a two-stage pipeline approach, which is dedicated to the generation of query suggestion candidates. We present three methods for generating candidate suggestions and apply them on multiple information sources. Using a purpose-built test collection, we find that these methods are able to generate high-quality suggestion candidates.