Machine Learning: Academic Discipline — Prolific Citations Library

Explore 9 peer-reviewed papers in Machine Learning (2018–2026). Academic research using Prolific for high-quality human data collection.

This page lists 9 peer-reviewed papers in the discipline of Machine Learning in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (9 of 9)

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
Unpacking Human Preference for LLMs: Demographically Aware Evaluation with the HUMAINE Framework

Authors: N Petrova, A Gordon, E Blindow

Year: 2026

Published in: Open review

Institution: Prolific

Research Area: Human-centered AI evaluation, Bayesian statistics, Responsible AI, AI alignment, LLM Evaluation

Discipline: Machine Learning, Artificial Intelligence

The study introduces HUMAINE, a multidimensional evaluation framework for LLMs, revealing demographic-specific preference variations and ranking google/gemini-2.5-pro as the top-performing model with a posterior probability of 95.6%.

Methods: Multi-turn naturalistic conversations analyzed using a hierarchical Bayesian Bradley-Terry-Davidson model with post-stratification to census data, stratified across 22 demographic groups.

Key Findings: Performance of 28 LLMs across five human-centric dimensions, accounting for demographic-specific preferences.

Sample Size: 23404
Impact of Tone-Aware Explanations in Recommender Systems

Authors: A Okoso, K Otaki, S Koide, Y Baba

Year: 2025

Published in: ACM Transactions on Recommender Systems, 2025•dl.acm.org

Institution: Toyota Central R and D Labs, Toyota

Research Area: Human-Computer Interaction (HCI)

Discipline: Machine Learning, Artificial Intelligence

The study demonstrates that tailoring the tone of textual explanations in recommender systems to domains and user attributes, such as age and personality traits, can enhance users' perceptions and engagement.

Methods: Two online user studies: (1) 470 participants evaluated synthetic explanations with six tones across three domains (movies, hotels, and home products), (2) 103 participants engaged with a real-world dataset from the hotel domain using a personalized recommender system.

Key Findings: The perceived effects of different textual explanation tones on users, examined across domains (movies, hotels, home products) and user attributes (e.g., age, personality traits).

DOI: https://dl.acm.org/doi/10.1145/3718101

Citations: 13

Sample Size: 573
Benchmarking World-Model Learning

Authors: A Warrier, D Nguyen, M Naim, M Jain, Y Liang, K Schroeder, C Yang, JB Tenenbaum, S Vollmer, K Ellis, Z Tavares

Year: 2025

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

Institution: Basis Research Institute, DFKI GmbH, Harvard University, Quebec AI Institute, University of Cambridge, Massachusetts Institute of Technology, Cornell University

Research Area: Agent learning, World Models, Benchmarking, Evaluation protocols, RLHF, LLM

Discipline: Computer Science, Artificial Intelligence, Machine Learning

The paper introduces WorldTest, a novel protocol for evaluating model-learning agents using reward-free exploration and behavior-based scoring, and demonstrates that humans outperform models on the AutumnBench suite of tasks, revealing significant gaps in world-model learning.

Methods: The authors proposed WorldTest, a protocol separating reward-free interaction from scored tests in related environments, with evaluations done using AutumnBench—a dataset of 43 grid-world environments and 129 tasks across prediction, planning, and causal dynamics.

Key Findings: Performance of model-learning agents and humans in acquiring world models for masked-frame prediction, planning, and understanding causal dynamics.

Citations: 1

Sample Size: 517
Whose view of safety? a deep dive dataset for pluralistic alignment of text-to-image models

Authors: C Rastogi, TH Teh, P Mishra, R Patel, D Wang, M Díaz, A Parrish, AM Davani, Z Ashwood

Year: 2025

Published in: arXiv preprint arXiv:2507.13383, 2025•arxiv.org

Institution: Google DeepMind, Google Research, Google

Research Area: AI alignment, safety evaluation, AI Safety, Multimodal evaluation, Human–AI interaction, LLM

Discipline: Computer Science, Machine Learning, Artificial Intelligence

This research introduces the DIVE dataset to enable pluralistic alignment in text-to-image models by accounting for diverse safety perspectives, revealing demographic variations in harm perception and advancing T2I model alignment strategies.

Methods: The study involved collecting feedback across 1000 prompts from demographically intersectional human raters to capture diverse safety perspectives, with an emphasis on empirical and contextual differences in harm perception.

Key Findings: Safety perceptions of text-to-image (T2I) model outputs from diverse demographic viewpoints and the influence of these perspectives on alignment strategies.

Citations: 1

Sample Size: 1000
The inadequacy of reinforcement learning from human feedback - radicalizing large language models via semantic vulnerabilities

Authors: TR McIntosh, T Susnjak, T Liu, P Watters

Year: 2024

Published in: ... on Cognitive and ..., 2024 - ieeexplore.ieee.org

Institution: Cyberoo, Massey University, Cyberstronomy, RMIT University

Research Area: Semantic Vulnerabilities in LLMs, Ideological Manipulation, Reinforcement Learning from Human Feedback (RLHF) Limitations

Discipline: Computer Science, Artificial Intelligence, Machine Learning

RLHF mechanisms are insufficient to prevent semantic manipulation of LLMs, allowing them to express extreme ideological viewpoints when subjected to targeted conditioning techniques.

Methods: Psychological semantic conditioning techniques were applied to assess the susceptibility of LLMs to ideological manipulation.

Key Findings: The ability of LLMs to resist or adopt extreme ideological viewpoints under semantic conditioning.

Citations: 219
ImagenHub: Standardizing the evaluation of conditional image generation models

Authors: M Ku, T Li, K Zhang, Y Lu, X Fu, W Zhuang

Year: 2024

Published in: - arXiv preprint arXiv …, 2023 - arxiv.org

Institution: University of Waterloo, Ohio State University, University of California Santa Barbara, University of Pensylvania

Research Area: AI alignment, Representation learning, Cognitive computational modeling, Vision foundation models evaluation, Multimodal, Vision models

Discipline: Computer Science, Artificial Intelligence, Machine Learning

This paper presents a method for **aligning machine vision model representations with human visual similarity judgments across different abstraction levels, improving how well models reflect human perceptual and conceptual organization and enhancing generalization and uncertainty prediction.

DOI: https://doi.org/10.48550/arXiv.2310.01596

Citations: 59
Safe RLHF: Safe reinforcement learning from human feedback

Authors: J Dai, X Pan, R Sun, J Ji, X Xu, M Liu, Y Wang

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Cornell University, Georgia Institute of Technology

Research Area: Reinforcement Learning from Human Feedback (RLHF), Safe AI, Reinforcement Learning

Discipline: Artificial Intelligence, Machine Learning

DOI: https://doi.org/10.48550/arXiv.2310.12773

Citations: 598
Making better use of the crowd: How crowdsourcing can advance machine learning research

Authors: JW Vaughan

Year: 2018

Published in: Journal of Machine Learning Research, 2018 - jmlr.org

Institution: Microsoft Research

Research Area: Crowdsourcing for Machine Learning Research, including data generation, model evaluation, hybrid intelligence systems, behavioral experiments.

Discipline: Machine Learning

Citations: 264