# Algorithmic Shortlisting in Participatory Budgeting

arXiv: https://arxiv.org/abs/2508.06577 · HTML: https://mlsanity.com/paper/2508.06577/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 50 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 27 of 27)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Time Travel in LLMs: Tracing Data Contamination in Large Language Models](https://mlsanity.com/paper/2308.08493/) (2023)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [Opportunities and Risks of LLMs for Scalable Deliberation with Polis](https://mlsanity.com/paper/2306.11932/) (2023)
- [Multi-Winner Voting with Approval Preferences](https://mlsanity.com/paper/2007.01795/) (2020)
- [Proportional Participatory Budgeting with Additive Utilities](https://mlsanity.com/paper/2008.13276/) (2020)
- [CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks](https://mlsanity.com/paper/2406.13945/) (2024)
- [Participatory Budgeting: Models and Approaches](https://mlsanity.com/paper/2003.00606/) (2020)
- [UrbanLLaVA: A Multi-modal Large Language Model for Urban Intelligence with Spatial Reasoning and Understanding](https://mlsanity.com/paper/2506.23219/) (2025)
- [Citizen Participation and Machine Learning for a Better Democracy](https://mlsanity.com/paper/2103.00508/) (2021)
- [Participatory Budgeting: Data, Tools, and Analysis](https://mlsanity.com/paper/2305.11035/) (2023)
- [Knapsack Voting for Participatory Budgeting](https://mlsanity.com/paper/2009.06856/) (2020)
- [The (Computational) Social Choice Take on Indivisible Participatory Budgeting](https://mlsanity.com/paper/2303.00621/) (2023)
- [Proportionally Representative Participatory Budgeting with Ordinal Preferences](https://mlsanity.com/paper/1911.00864/) (2019)
- [Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting](https://mlsanity.com/paper/2310.03501/) (2023)
- [A Framework for Approval-based Budgeting Methods](https://mlsanity.com/paper/1809.04382/) (2018)
- [Scaling Human Judgment in Community Notes with LLMs](https://mlsanity.com/paper/2506.24118/) (2025)
- [Enabling the Digital Democratic Revival: A Research Program for Digital Democracy](https://mlsanity.com/paper/2401.16863/) (2024)
- [Using Collective Dialogues and AI to Find Common Ground Between Israeli and Palestinian Peacebuilders](https://mlsanity.com/paper/2503.01769/) (2025)
- [Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland](https://mlsanity.com/paper/2111.11766/) (2021)
- [Shortlisting Rules and Incentives in an End-to-End Model for Participatory Budgeting](https://mlsanity.com/paper/2010.10309/) (2020)
- [Agent-Mediated Social Choice](https://mlsanity.com/paper/1806.07199/) (2018)

## Abstract (arXiv)

Participatory budgeting is a democratic innovation that allows citizens to propose and vote on public investment projects. To help organizers manage large volumes of submissions, we design and test privacy-preserving methods for algorithmic shortlisting. These algorithms predict which projects are likely to be funded using only project features and anonymous historical voting data. We demonstrate the limitations of a naive approach that uses a large language model to rank projects based on past success and propose a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning. Our findings indicate that user preferences in participatory budgeting are stable enough to allow algorithmic shortlisting to approximate an initial selection of projects effectively.