# Pseudo-Query Reformulation

arXiv: https://arxiv.org/abs/1507.03928 · HTML: https://mlsanity.com/paper/1507.03928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2015.

## Built on by (most cited first, 1 of 1)

- [Think Before You Retrieve: Learning Test-Time Adaptive Search with Small Language Models](https://mlsanity.com/paper/2511.07581/) (2025)

## Abstract (arXiv)

Automatic query reformulation refers to rewriting a user's original query in order to improve the ranking of retrieval results compared to the original query. We present a general framework for automatic query reformulation based on discrete optimization. Our approach, referred to as pseudo-query reformulation, treats automatic query reformulation as a search problem over the graph of unweighted queries linked by minimal transformations (e.g. term additions, deletions). This framework allows us to test existing performance prediction methods as heuristics for the graph search process. We demonstrate the effectiveness of the approach on several publicly available datasets.