# The Surprising Effectiveness of Rankers Trained on Expanded Queries

arXiv: https://arxiv.org/abs/2404.02587 · HTML: https://mlsanity.com/paper/2404.02587/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [InPars: Data Augmentation for Information Retrieval using Large Language Models](https://mlsanity.com/paper/2202.05144/) (2022)
- [Generative Query Reformulation for Effective Adhoc Search](https://mlsanity.com/paper/2308.00415/) (2023)
- [How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset](https://mlsanity.com/paper/2105.07975/) (2021)
- [Query Performance Prediction: From Ad-hoc to Conversational Search](https://mlsanity.com/paper/2305.10923/) (2023)
- [Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction](https://mlsanity.com/paper/2202.07376/) (2022)
- [Relevance-based Word Embedding](https://mlsanity.com/paper/1705.03556/) (2017)
- [Identifying Unclear Questions in Community Question Answering Websites](https://mlsanity.com/paper/1901.06168/) (2019)
- [Extractive Explanations for Interpretable Text Ranking](https://mlsanity.com/paper/2106.12460/) (2021)
- [Data Augmentation for Sample Efficient and Robust Document Ranking](https://mlsanity.com/paper/2311.15426/) (2023)
- [Selective Query Processing: a Risk-Sensitive Selection of System Configurations](https://mlsanity.com/paper/2305.18311/) (2023)
- [An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking](https://mlsanity.com/paper/2103.16669/) (2021)
- [Supervised Contrastive Learning Approach for Contextual Ranking](https://mlsanity.com/paper/2207.03153/) (2022)

## Abstract (arXiv)

An important problem in text-ranking systems is handling the hard queries that form the tail end of the query distribution. The difficulty may arise due to the presence of uncommon, underspecified, or incomplete queries. In this work, we improve the ranking performance of hard or difficult queries without compromising the performance of other queries. Firstly, we do LLM based query enrichment for training queries using relevant documents. Next, a specialized ranker is fine-tuned only on the enriched hard queries instead of the original queries. We combine the relevance scores from the specialized ranker and the base ranker, along with a query performance score estimated for each query. Our approach departs from existing methods that usually employ a single ranker for all queries, which is biased towards easy queries, which form the majority of the query distribution. In our extensive experiments on the DL-Hard dataset, we find that a principled query performance based scoring method using base and specialized ranker offers a significant improvement of up to 25% on the passage ranking task and up to 48.4% on the document ranking task when compared to the baseline performance of using original queries, even outperforming SOTA model.