# Ranking-Incentivized Quality Preserving Content Modification

arXiv: https://arxiv.org/abs/2005.12989 · HTML: https://mlsanity.com/paper/2005.12989/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2020.

## Built on by (most cited first, 5 of 5)

- [Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models](https://mlsanity.com/paper/2209.06506/) (2022)
- [Towards Imperceptible Document Manipulations against Neural Ranking Models](https://mlsanity.com/paper/2305.01860/) (2023)
- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization](https://mlsanity.com/paper/2602.12187/) (2026)
- [TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness](https://mlsanity.com/paper/2512.01183/) (2025)

## Abstract (arXiv)

The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings. We present an automatic method for quality-preserving modification of document content -- i.e., maintaining content quality -- so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed. The method replaces a passage in the document with some other passage. To select the two passages, we use a learning-to-rank approach with a bi-objective optimization criterion: rank promotion and content-quality maintenance. We used the approach as a bot in content-based ranking competitions. Analysis of the competitions demonstrates the merits of our approach with respect to human content modifications in terms of rank promotion, content-quality maintenance and relevance.