# Neural Passage Quality Estimation for Static Pruning

arXiv: https://arxiv.org/abs/2407.12170 · HTML: https://mlsanity.com/paper/2407.12170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.14546/) (2026)
- [Document Quality Scoring for Web Crawling](https://mlsanity.com/paper/2504.11011/) (2025)
- [Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors](https://mlsanity.com/paper/2606.02814/) (2026)

## Abstract (arXiv)

Neural networks -- especially those that use large, pre-trained language models -- have improved search engines in various ways. Most prominently, they can estimate the relevance of a passage or document to a user's query. In this work, we depart from this direction by exploring whether neural networks can effectively predict which of a document's passages are unlikely to be relevant to any query submitted to the search engine. We refer to this query-agnostic estimation of passage relevance as a passage's quality. We find that our novel methods for estimating passage quality allow passage corpora to be pruned considerably while maintaining statistically equivalent effectiveness; our best methods can consistently prune >25% of passages in a corpora, across various retrieval pipelines. Such substantial pruning reduces the operating costs of neural search engines in terms of computing resources, power usage, and carbon footprint -- both when processing queries (thanks to a smaller index size) and when indexing (lightweight models can prune low-quality passages prior to the costly dense or learned sparse encoding step). This work sets the stage for developing more advanced neural "learning-what-to-index" methods.