Work

Automatic Document Editing for Improved Ranking

Year: 2025

Type: conference-paper

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.

Source: International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

DOI: 10.1145/3726302.3730168

Authors: Niv Bardas, Tommy Mordo, Oren Kurland, Moshe Tennenholtz

Institution: Technion – Israel Institute of Technology

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Universitas Brawijaya

Awards: G204204078, G434337789, G5644739299, G6396787490