Browse 26 peer-reviewed papers from Massachusetts Institute Of Technology spanning Human-AI Interaction, Large Language Models (2024–2026). Research powered by Prolific's high-quality participant data.
This page lists 26 peer-reviewed papers from researchers at Massachusetts Institute Of Technology in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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
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Authors: M Groh, A Sankaranarayanan, N Singh, DY Kim
Year: 2025
Published in: Nature ..., 2024 - nature.com
Institution: Northwestern University, Massachusetts Institute of Technology
Research Area: Deepfakes, Media Forensics, Human Perception of AI-Generated Content, Political Communication
Discipline: Computational Social Science
Humans are better at detecting deepfake political speeches using audio-visual cues than relying on text alone; state-of-the-art text-to-speech audio makes deepfakes harder to discern.
Methods: Five pre-registered randomized experiments with varied base rates of misinformation, audio sources, question framings, and media modalities were conducted.
Key Findings: Human accuracy in discerning real political speeches from deepfakes across media formats and contextual variables.
DOI: https://doi.org/10.1038/s41467-024-51998-z
Citations: 63
Sample Size: 2215
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Authors: S Shekar, P Pataranutaporn, C Sarabu, GA Cecchi
Year: 2025
Published in: NEJM AI, 2025 - ai.nejm.org
Institution: MIT Media Lab, IBM Research, Stanford University, Massachusetts Institute of Technology
Research Area: AI Ethics, Healthcare, Patient Trust, Medical Misinformation
Discipline: Artificial Intelligence, Human-Computer Interaction, AI Ethics
This paper discusses a study by MIT researchers detailing patient trust in AI-generated medical advice, even when that advice is incorrect, raising concerns about misinformation in healthcare.
Citations: 19
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Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Natural Language Processing
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
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Authors: N Aldahoul, H Ibrahim, M Varvello, A Kaufman
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Delft University of Technology, University of Pennsylvania, New York University, King Abdullah University of Science and Technology, Massachusetts Institute of Technology, University of Texas at Austin
Research Area: Artificial Intelligence, Computer Science, Political Science
Discipline: Artificial Intelligence, Social Science
The study finds that Large Language Models (LLMs) exhibit extreme political views on specific topics despite appearing ideologically moderate overall, and demonstrate a persuasive influence on users' political preferences even in informational contexts.
Methods: Compared 31 LLMs' political biases against benchmarks (legislators, judges, representative voter samples) and conducted a randomized experiment to measure their persuasive impact in informational interactions.
Key Findings: Ideological consistency, political extremity, and persuasive effects of LLMs in information-seeking contexts.
Citations: 7
Sample Size: 31
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Authors: H Lin, G Czarnek, B Lewis, JP White, AJ Berinsky
Year: 2025
Published in: Nature, 2025 - nature.com
Institution: Massachusetts Institute of Technology
Research Area: Political Persuasion, Human-AI Dialogue, Electoral Behavior
Discipline: Political Science, Artificial Intelligence
The study shows that AI-driven dialogues can significantly influence voter attitudes and candidate preferences, with persuasion effects surpassing traditional political advertisements, though inaccuracies were more prevalent in content generated by AI models supporting right-wing candidates.
Methods: Pre-registered experiments where participants interacted with AI advocating for one of two candidates or a ballot measure, examining persuasion strategies and effects across three elections.
Key Findings: Influence of AI-generated dialogues on voter attitudes and preferences, including analysis of persuasion strategies and accuracy of presented information.
Citations: 3
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Authors: A Karamolegkou, O Eberle, P Rust, C Kauf, A Søgaard
Year: 2025
Published in: ArXiv
Institution: Aleph Alpha, Massachusetts Institute of Technology
Research Area: Adversarial Ambiguity, Language Model Evaluation, Artificial Intelligence, Natural Language Processing, Large Language Models, AI Evaluation, Red Teaming
Discipline: Natural Language Processing
The paper assesses language models' sensitivity to ambiguity using an adversarial dataset and finds that direct prompting poorly identifies ambiguity, while linear probes achieve high accuracy in decoding ambiguity from model representations.
Methods: An adversarial ambiguity dataset was introduced with various types of ambiguities and transformations; models were tested using direct prompts and linear probes trained on internal representations.
Key Findings: Language models' ability to detect ambiguity, including syntactic, lexical, and phonological types, as well as performance under adversarial variations.
Citations: 2
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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, Reinforcement Learning from Human Feedback (RLHF), Large Language Models
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
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Authors: P Cooper, A Lim, J Irons, M McGrath, H Jarvis
Year: 2025
Published in: Proceedings of the ..., 2025 - dl.acm.org
Institution: Microsoft Research, Massachusetts Institute of Technology, University of Washington
Research Area: Human-AI Interaction, Trust in AI
Discipline: Human-Computer Interaction
Trust in AI dynamically influences users' reliance on AI advice during a deepfake detection task, with no significant impact observed from the timing of AI advice delivery.
Methods: Researchers conducted an online study with participants performing a deepfake detection task, comparing performance across conditions where AI advice was provided either concurrently with decisions or after an initial evaluation. Computational modeling was used to analyze trust dynamics.
Key Findings: Impact of AI advice and its timing on task performance, and the dynamic role of user trust in AI based on expectations of its ability.
DOI: https://dl.acm.org/doi/10.1145/3706599.3719870
Citations: 1
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Authors: K Vodrahalli, R Daneshjou, T Gerstenberg
Year: 2024
Published in: Proceedings of the 2022 ..., 2022 - dl.acm.org
Institution: Stanford University, Massachusetts Institute of Technology
Research Area: Trust in AI, Human-AI Interaction, Decision Making
Discipline: Human-AI Interaction, Decision Science
Humans' trust in AI advice is influenced by their beliefs about AI performance, and once they accept AI advice, they treat it similarly to advice from human peers.
Methods: Crowdworkers participated in several experimental settings to evaluate how participants respond to AI versus human suggestions and characterize user behavior with a proposed activation-integration model.
Key Findings: The influence of AI advice compared to human advice on decision-making and the behavioral factors affecting the use of such advice.
DOI: 10.1145/3514094.3534150
Citations: 99
Sample Size: 1100
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Authors: GKM Liu
Year: 2024
Published in: Massachusetts Institute of Technology, 2023 - computing.mit.edu
Institution: Massachusetts Institute of Technology
Research Area: Reinforcement Learning from Human Feedback (RLHF), Human-AI Interaction
Discipline: Artificial Intelligence
The paper explores Reinforcement Learning with Human Feedback (RLHF) as a transformative tool to align AI with human values, mitigate bias, and democratize technology, while emphasizing its societal implications and ethical considerations.
Methods: The paper employs a systematic study of existing and potential societal effects of RLHF, guided by key questions addressing ethical, social, and practical impacts.
Key Findings: The study investigates how RLHF affects information integrity, societal values, social equity, access to AI, cultural relations, industrial transformation, and labor dynamics.
Citations: 17
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Authors: LS Treiman, CJ Ho, W Kool
Year: 2024
Published in: Proceedings of the National Academy of ..., 2024 - pnas.org
Institution: Massachusetts Institute of Technology, Yale University, Washington University in St. Louis
Research Area: AI Ethics, Behavioral Economics, Decision-Making in AI Systems
Discipline: Artificial Intelligence, Behavioral Science
People alter their behavior when they know their actions will train AI, leading to unintentional habits and biased training data for AI systems.
Methods: Five studies were conducted using the ultimatum game; participants were tasked with deciding on monetary splits proposed by either humans or AI, with some informed their decisions would train the AI.
Key Findings: Behavioral changes in participants when training AI, persistence of these changes over time, and implications for AI training bias.
DOI: https://doi.org/10.1073/pnas.2408731121
Citations: 13
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Authors: M Shin, J Kim
Year: 2024
Published in: Available at SSRN 4725351, 2024 - researchgate.net
Institution: Massachusetts Institute of Technology, Yale University
Research Area: Linguistic Feature Alignment, Persuasion, Large Language Models
Discipline: Artificial Intelligence, Computational Social Science
Citations: 11
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Authors: A Berke, R Mahari, A Pentland, K Larson
Year: 2024
Published in: Proceedings of the ACM ..., 2024 - dl.acm.org
Institution: Stanford's CodeX Center, Harvard Law School, MIT Media Lab, Stanford Institute for Human-Centered AI, The Larson Institute, Massachusetts Institute of Technology, Stanford University
Research Area: Crowdsourcing, Transparency, Human-Computer Interaction (HCI) in Social Science Research
Discipline: Computational Social Science, Human-Computer Interaction
DOI: https://dl.acm.org/doi/abs/10.1145/3687005
Citations: 9
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Authors: P Schoenegger, P Park, E Karger, P Tetlock
Year: 2024
Published in: ArXiv
Institution: Federal Reserve Bank of Chicago, London School of Economics and Political Science, Massachusetts Institute of Technology, University of Pennsylvania
Research Area: LLM Assistants, Human Forecasting, Predictive Modeling, AI-Augmented Decision Making, Large Language Models
Discipline: Artificial Intelligence, Behavioral Science
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Authors: E Jahani, B Manning, J Zhang, H TuYe, M Alsobay, C Nicolaides, S Suri, D Holtz
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, Microsoft Research, Stanford University, University of California Berkeley, University of Cyprus, University of Maryland
Research Area: Human-AI Interaction, Generative AI, Prompt Engineering
Discipline: Artificial Intelligence, focusing on Human-AI Interaction, Generative AI
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Authors: M Svanberg, W Li, M Fleming, B Goehring, N Thompson
Year: 2024
Published in: SSRN
Institution: IBM Research, Massachusetts Institute of Technology, The Productivity Institute, CSAIL
Research Area: Computer Vision, Economics of IT, Deep Learning Systems
Discipline: Artificial Intelligence
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Authors: Z Qiu, W Liu, H Feng, Z Liu, T Xiao
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, Max Planck Institute, University of Cambridge
Research Area: Computational cognition, LLM evaluation, Program synthesis, Multimodal reasoning
Discipline: Artificial Intelligence
Introduces SGP-Bench, a benchmark testing whether LLMs can answer semantic and spatial questions about images purely from graphics programs (SVG/CAD), effectively probing “visual imagination without vision.” The authors show current LLMs struggle - sometimes near chance - even when images are trivial for humans, but demonstrate that Symbolic Instruction Tuning (SIT) can meaningfully improve thi...
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Authors: Daria Kryvosheieva
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology
Research Area: Natural Language Processing, AI Evaluation
Discipline: Natural Language Processing
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Authors: Samantha Chan, Pat Pataranutaporn, Aditya Suri, Wazeer Zulfikar, Pattie Maes
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology, University of California
Research Area: Cognitive Psychology, False Memory Research, Human-AI Interaction, Conversational AI
Discipline: Cognitive Psychology, Artificial Intelligence
