# Generating Query Suggestions to Support Task-Based Search

arXiv: https://arxiv.org/abs/1708.08289 · HTML: https://mlsanity.com/paper/1708.08289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## 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 problem of generating query suggestions to support users in completing their underlying tasks (which motivated them to search in the first place). Given an initial query, these query suggestions should provide a coverage of possible subtasks the user might be looking for. We propose a probabilistic modeling framework that obtains keyphrases from multiple sources and generates query suggestions from these keyphrases. Using the test suites of the TREC Tasks track, we evaluate and analyze each component of our model.