Browse 4 peer-reviewed papers from Intel spanning Experimental evaluation, RCT (2022–2025). Research powered by Prolific's high-quality participant data.
This page lists 4 peer-reviewed papers from researchers at Intel in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: L Luettgau, HR Kirk, K Hackenburg, J Bergs, H Davidson, H Ogden, D Siddarth, S Huang
Year: 2025
Published in: ARXIV
Institution: AI Security Institute, I Policy Directorate, Collective Intelligence Project, Anthropic
Research Area: Experimental evaluation, RCT, Survey Research
Discipline: Computer Science, Human–Computer Interaction (HCI)
Conversational AI is as effective as self-directed internet searches in increasing political knowledge, reducing misinformation beliefs, and promoting accuracy among users in the UK during the 2024 election period.
Methods: A national survey (N=2,499) measured conversational AI usage for political information-seeking, followed by a series of randomised controlled trials (N=2,858) comparing conversational AI to self-directed internet search in improving political knowledge.
Key Findings: Extent of conversational AI usage for political knowledge-seeking in the UK and its efficacy in enhancing political knowledge and reducing misinformation compared to traditional internet searches.
Citations: 3
Sample Size: 5357
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Authors: T Kaufmann, P Weng, V Bengs, E Hüllermeier
Year: 2024
Published in: 2024 - epub.ub.uni-muenchen.de
Institution: Paderborn University, German Research Center for Artificial Intelligence (DFKI), Duke Kunshan University
Research Area: Reinforcement Learning from Human Feedback (RLHF), LLM, Reward Modeling
Discipline: Artificial Intelligence
This paper surveys the fundamentals, diverse applications, and evolving impact of reinforcement learning from human feedback (RLHF), emphasizing its role in improving intelligent system alignment and performance.
Methods: The paper utilizes a survey-based approach to synthesize existing research, exploring the interactions between reinforcement learning algorithms and human input.
Key Findings: The study examines the principles, dynamics, applications, and trends in RLHF, offering insights into its role in enhancing large language models (LLMs) and intelligent systems.
Citations: 354
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Authors: O Raccah, P Chen, TM Gureckis, D Poeppe, VA Vo
Year: 2024
Published in: Nature
Institution: Intel Labs, New York University, Yale University
Research Area: Cognitive Psychology, Memory Research, Natural Language Processing (NLP)
Discipline: Psychology, Artificial Intelligence
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Authors: K Vodrahalli, T Gerstenberg
Year: 2022
Published in: Advances in Neural ..., 2022 - proceedings.neurips.cc
Institution: Columbia University, Princeton University, Intel, Stanford University, Massachusetts Institute of Technology
Research Area: Human-AI Collaboration, Human Behavior Modeling, Decision Making
Discipline: Artificial Intelligence
Citations: 70