Meta: Research Institution — Prolific Citations Library

Browse 3 peer-reviewed papers from Meta spanning Probabilistic reasoning, Bayesian cognition (2024–2026). Research powered by Prolific's high-quality participant data.

This page lists 3 peer-reviewed papers from researchers at Meta in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

Bayesian teaching enables probabilistic reasoning in large language models

Authors: L Qiu, F Sha, K Allen, Y Kim, T Linzen, S van Steenkiste

Year: 2026

Published in: Nature …, 2026 - nature.com

Institution: Meta, Google DeepMind, Massachusetts Institute of Technology, Google Research, Google

Research Area: Probabilistic reasoning, Bayesian cognition, Neural language models, Reasoning, AI Evaluations

Discipline: Machine learning, Artificial intelligence

This paper sits at the intersection of machine learning and computational cognitive science, showing that large language models can acquire generalized probabilistic reasoning by being trained to imitate Bayesian belief updating rather than relying on prompting or heuristics.

Citations: 8
Incentivizing High-Quality Human Annotations with Golden Questions

Authors: S Liu, Z Cai, H Wang, Z Ma, X Li

Year: 2025

Published in: arXiv preprint arXiv:2505.19134, 2025 - arxiv.org

Institution: Meta, Imperial College London

Research Area: Artificial Intelligence, Crowdsourcing, LLM

Discipline: Artificial Intelligence

The paper develops a principal-agent model to incentivize high-quality human annotations using golden questions and identifies criteria for these questions to effectively monitor annotators' performance.

Methods: The authors use a principal-agent model with maximum likelihood estimators (MLE) and hypothesis testing to design incentive-compatible systems for annotators. Golden questions of high certainty and similar format to normal data were selected and validated through experiments.

Key Findings: The effectiveness of golden questions for incentivizing and monitoring high-quality human annotations in preference data.

DOI: https://doi.org/10.48550/arXiv.2505.19134

Citations: 1
The PRISM alignment dataset: What participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large ...

Authors: HR Kirk, M Bartolo, A Whitefield, P Rottger

Year: 2024

Published in: Advances in ..., 2024 - proceedings.neurips.cc

Institution: Meta, Cohere, AWS AI Labs, Contextual AI, Factored AI, University of Oxford, Bocconi University, Meedan, Hugging Face, University College London, ML Commons, University of Pennsylvania

Research Area: LLM Alignment, Human Feedback, Multicultural Studies

Discipline: Artificial Intelligence, Computational Social Science

The PRISM Alignment Dataset presents a large-scale, culturally diverse human feedback dataset linking sociodemographic profiles of 1,500 participants from 75 countries to their contextual preferences and fine‑grained ratings in 8,011 live conversations with 21 LLMs. This enables analysis of how subjective values vary across people and cultures in LLM alignment data.

DOI: https://doi.org/10.52202/079017-3342

Citations: 204