# Dominantly Truthful Multi-task Peer Prediction with a Constant Number of Tasks

arXiv: https://arxiv.org/abs/1911.00272 · HTML: https://mlsanity.com/paper/1911.00272/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [A Bayesian Framework for Information-Theoretic Probing](https://mlsanity.com/paper/2109.03853/) (2021)
- [Eliciting Informative Text Evaluations with Large Language Models](https://mlsanity.com/paper/2405.15077/) (2024)
- [Incentivizing Truthful Language Models via Peer Elicitation Games](https://mlsanity.com/paper/2505.13636/) (2025)
- [Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation](https://mlsanity.com/paper/2203.04156/) (2022)
- [Surprisingly Popular Voting Recovers Rankings, Surprisingly!](https://mlsanity.com/paper/2105.09386/) (2021)
- [Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms](https://mlsanity.com/paper/2402.13567/) (2024)
- [Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth](https://mlsanity.com/paper/2506.06991/) (2025)
- [Data Reliability Scoring](https://mlsanity.com/paper/2510.17085/) (2025)
- [Truthfulness Despite Weak Supervision: Evaluating and Training LLMs Using Peer Prediction](https://mlsanity.com/paper/2601.20299/) (2026)
- [Stochastically Dominant Peer Prediction](https://mlsanity.com/paper/2506.02259/) (2025)
- [Information Elicitation Meets Clustering](https://mlsanity.com/paper/2110.00952/) (2021)
- [Scoring Rules! Statistical and Strategic Alignment for Text Evaluation Metrics](https://mlsanity.com/paper/2608.01423/) (2026)

## Abstract (arXiv)

In the setting where participants are asked multiple similar possibly subjective multi-choice questions (e.g. Do you like Panda Express? Y/N; do you like Chick-fil-A? Y/N), a series of peer prediction mechanisms are designed to incentivize honest reports and some of them achieve dominantly truthfulness: truth-telling is a dominant strategy and strictly dominate other "non-permutation strategy" with some mild conditions. However, a major issue hinders the practical usage of those mechanisms: they require the participants to perform an infinite number of tasks. When the participants perform a finite number of tasks, these mechanisms only achieve approximated dominant truthfulness. The existence of a dominantly truthful multi-task peer prediction mechanism that only requires a finite number of tasks remains to be an open question that may have a negative result, even with full prior knowledge. This paper answers this open question by proposing a new mechanism, Determinant based Mutual Information Mechanism (DMI-Mechanism), that is dominantly truthful when the number of tasks is at least 2C and the number of participants is at least 2. C is the number of choices for each question (C=2 for binary-choice questions). In addition to incentivizing honest reports, DMI-Mechanism can also be transferred into an information evaluation rule that identifies high-quality information without verification when there are at least 3 participants. To the best of our knowledge, DMI-Mechanism is the first dominantly truthful mechanism that works for a finite number of tasks, not to say a small constant number of tasks.