Reinforcement Learning With Human Feedback Rlhf: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Reinforcement Learning With Human Feedback Rlhf (2024–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Reinforcement Learning With Human Feedback Rlhf in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Influencing Humans to Conform to Preference Models for RLHF

Authors: S Hatgis-Kessell, WB Knox, S Booth, S Niekum

Year: 2025

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

Institution: Stanford University, UMass Amherst, Carnegie Mellon University

Research Area: Reinforcement Learning with Human Feedback (RLHF)

Discipline: Artificial Intelligence, Human-Computer Interaction (HCI)

The paper investigates whether human preferences can be influenced to align more closely with assumed preference models in RLHF algorithms through interventions such as showing model-derived quantities, training on preference models, and modifying elicitation questions.

Methods: Three human studies were conducted where interventions were tested, including revealing model-derived quantities, training participants on a preference model, and altering how preference questions were framed.

Key Findings: Evaluated the impact of interventions on humans' expression of preferences to align better with the assumed preference models of RLHF algorithms.

DOI: https://doi.org/10.48550/arXiv.2501.06416

Citations: 1
Transforming human interactions with AI via reinforcement learning with human feedback (RLHF)

Authors: GKM Liu

Year: 2024

Published in: Massachusetts Institute of Technology, 2023 - computing.mit.edu

Institution: Massachusetts Institute of Technology

Research Area: Reinforcement Learning with 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