Automatic Document Editing for Improved Ranking

We present a study of using large language models (LLMs) to modify a document so as to have it highly ranked for a query by an undisclosed ranking function. We present different prompting methods inspired by work on using LLMs to induce ranking. Empirical evaluation attests to the merits of the best performing methods with respect to human modifications and a highly effective feature-based modification method.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3730168
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

Automatic Document Editing for Improved Ranking

Moshe Tennenholtz, Oren Kurland, Tommy Mordo, Niv Bardas
Topic Modeling
article

Automatic Document Editing for Improved Ranking

Moshe Tennenholtz, Oren Kurland, Tommy Mordo, Niv Bardas
article en

Abstract

We present a study of using large language models (LLMs) to modify a document so as to have it highly ranked for a query by an undisclosed ranking function. We present different prompting methods inspired by work on using LLMs to induce ranking. Empirical evaluation attests to the merits of the best performing methods with respect to human modifications and a highly effective feature-based modification method.

Technion – Israel Institute of Technology (IL)
European Commission
Openalex Percentile: Top 92%
Topic Modeling
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Automatic Document Editing for Improved Ranking — Moshe Tennenholtz, Oren Kurland, et al. · (2025) | TGRS Research Map | TGRS