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University Of Massachusetts Amherst Papers

0 research papers from University Of Massachusetts Amherst that used Prolific for participant recruitment.

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University Of Massachusetts Amherst

University Of Massachusetts Amherst: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from University Of Massachusetts Amherst spanning Reinforcement Learning from Human Feedback (RLHF), Large Language Models (2024–2025). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at University Of Massachusetts Amherst in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

RLHF deciphered: A critical analysis of reinforcement learning from human feedback for LLMs

Authors: S Chaudhari, P Aggarwal, V Murahari

Year: 2025

Published in: ACM Computing ..., 2025 - dl.acm.org

Institution: University of Massachusetts Amherst, Carnegie Mellon University, Princeton University

Research Area: Reinforcement Learning from Human Feedback (RLHF), Large Language Models

Discipline: Artificial Intelligence

The paper critically analyzes reinforcement learning from human feedback (RLHF) for large language models (LLMs), emphasizing the importance and limitations of reward models in improving human-aligned AI systems.

Methods: Analyzed RLHF frameworks through reinforcement learning principles; conducted a categorical literature review to identify modeling challenges, assumptions, and framework limitations.

Key Findings: Investigated RLHF's fundamentals, focusing on the role of reward models, implications of design choices in RLHF training algorithms, and underlying issues like generalization errors, model misspecification, and feedback sparsity.

Citations: 117
Detecting the corruption of online questionnaires by artificial intelligence

Authors: B Lebrun, S Temtsin, A Vonasch

Year: 2024

Published in: Frontiers in Robotics and ..., 2024 - frontiersin.org

Institution: University of Lausanne, University of California Berkeley, University of Massachusetts Amherst, Arizona State University

Research Area: AI in Social Science Research, Survey Methodology, Data Quality

Discipline: Artificial Intelligence

The study examines the integrity of online questionnaire responses and concludes that humans can identify AI-generated text with 76% accuracy, but current AI detection systems are ineffective, raising concerns about data quality in online surveys.

Methods: Human participants and automatic AI detection systems were tested on their ability to differentiate AI-generated text from human-generated text in the context of online questionnaires.

Key Findings: The study measured the ability of humans and AI detection tools to correctly identify whether text was generated by a human or an AI system for online questionnaire responses.

DOI: https://doi.org/10.3389/frobt.2023.1277635

Citations: 26