# Peer Truth Serum: Incentives for Crowdsourcing Measurements and Opinions

arXiv: https://arxiv.org/abs/1704.05269 · HTML: https://mlsanity.com/paper/1704.05269/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2017.

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

- [Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms](https://mlsanity.com/paper/2402.13567/) (2024)
- [On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models](https://mlsanity.com/paper/2311.07692/) (2023)
- [Stochastically Dominant Peer Prediction](https://mlsanity.com/paper/2506.02259/) (2025)
- [Incentivizing Truthfulness and Collaborative Fairness in Bayesian Learning](https://mlsanity.com/paper/2605.11889/) (2026)
- [Mitigating LLM sycophancy with RL-based fine-tuning: Bayesian Truth Serum approach](https://mlsanity.com/paper/2608.25267/) (2026)

## Abstract (arXiv)

Modern decision making tools are based on statistical analysis of abundant data, which is often collected by querying multiple individuals. We consider data collection through crowdsourcing, where independent and self-interested agents, non-experts, report measurements, such as sensor readings, opinions, such as product reviews, or answers to human intelligence tasks. Since the accuracy of information is positively correlated with the effort invested in obtaining it, self-interested agents tend to report low-quality data. Therefore, there is a need for incentives that cover the cost of effort, while discouraging random reports. We propose a novel incentive mechanism called Peer Truth Serum that encourages truthful and accurate reporting, showing that it is the unique mechanism to satisfy a combination of desirable properties.