Discover 16 peer-reviewed studies in Natural Language Processing (2022–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 16 peer-reviewed papers in the research area of Natural Language Processing in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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: 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: T Hu, N Collier
Year: 2025
Published in: arXiv preprint arXiv:2503.03335, 2025 - arxiv.org
Institution: University of Cambridge
Research Area: Affective Computing, Natural Language Processing, Computational Social Science
Discipline: Computational Social Science
The iNews dataset is a multimodal resource for studying personalized affective responses to news, improving modeling accuracy by incorporating annotator persona metadata.
Methods: 292 demographically diverse UK participants annotated 2,899 Facebook news posts with multidimensional labels (e.g., emotions, valence, arousal), combined with comprehensive participant persona data.
Key Findings: Modeled personalized affective responses to news through annotations capturing valence, arousal, emotions, and persona metadata.
Citations: 2
Sample Size: 2899
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Authors: P Schmidtová, O Dušek, S Mahamood
Year: 2025
Published in: ArXiv
Institution: Charles University, Trivago
Research Area: Summarization evaluation, Natural Language Processing, LLM-as-a-Judge, AI Evaluation
Discipline: Natural Language Processing
Simpler metrics like word overlap surprisingly correlate well with human judgments in summarization evaluation, outperforming complex methods in out-of-domain applications, though LLMs remain unreliable for assessment due to annotation biases.
Methods: Human evaluation campaigns with categorical error assessment, span-level annotations, and comparison of traditional metrics, trainable models, and LLM-as-a-judge approaches.
Key Findings: Effectiveness of summarization evaluation methods and their correlation with human judgment, along with business impacts of incorrect information in generated summaries.
Citations: 1
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Authors: Elyas Meguellati1, Assad Zeghina2, Shazia Sadiq1, Gianluca Demartini1
Year: 2025
Published in: ArXiv
Institution: University of Queensland, University of Strasbourg
Research Area: Natural Language Processing, Harmful Content Detection
Discipline: Natural Language Processing
The paper introduces an approach using LLM-based semantic augmentation for harmful content detection on social media, achieving performance comparable to human-annotated models but at reduced cost.
Methods: The researchers utilize LLMs to clean noisy text and generate explanations for context-rich preprocessing, then evaluate the augmented training sets on multiple high-context datasets such as SemEval 2024 Persuasive Meme, Google Jigsaw toxic comments, and Facebook hateful memes datasets.
Key Findings: The efficacy of LLM-based semantic augmentation in enhancing training sets for social media tasks such as propaganda detection, hateful meme classification, and toxicity identification.
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Authors: S Herbold, A Trautsch, Z Kikteva, A Kaufman
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: University of Passau
Research Area: Natural Language Processing, Artificial Intelligence, Machine Learning
Discipline: Artificial Intelligence, Political Science, Natural Language Processing
Citations: 7
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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: Md. Khairul Islam1, Andrew Wang1, Tianhao Wang1, Yangfeng Ji1, Judy Fox 1, Jieyu Zhao2
Year: 2024
Published in: ArXiv
Institution: University of Virginia
Research Area: Differential Privacy, Bias Mitigation, Large Language Models, Natural Language Processing, AI Bias
Discipline: Artificial Intelligence, Natural Language Processing
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Authors: Yuxin Wang♣ Xiaomeng Zhu◆ Weimin Lyu♠∗ Saeed Hassanpour♣ Soroush Vosoughi♣
Year: 2024
Published in: ArXiv
Institution: Department of Computer Science Dartmouth College, Stony Brook University, Yale University
Research Area: Natural Language Processing, Computational Linguistics
Discipline: Natural Language Processing
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Authors: Jacob Beck, Stephanie Eckman, Bolei Ma, Rob Chew, Frauke Kreuter
Year: 2024
Published in: ACL Anthology
Institution: University of Maryland
Research Area: Annotation Sensitivity, Order Effects, Natural Language Processing, Social Science in AI
Discipline: Natural Language Processing, Computational Social Science
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Authors: Quan Ze Chen K.J. Kevin Feng Chan Young Park Amy X. Zhang
Year: 2024
Published in: ArXiv
Institution: University of Washington
Research Area: In-Context Learning, Computational Linguistics, Natural Language Processing
Discipline: Computer Science, Computational Linguistics, Natural Language Processing
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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
Discipline: Psychology, Artificial Intelligence
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Authors: J Barua, S Subramanian, K Yin, A Suhr
Year: 2024
Published in: ArXiv
Institution: University of California Berkeley
Research Area: Natural Language Processing, Machine Translation, Lexical Semantics
Discipline: Natural Language Processing
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Authors: J Pei, D Jurgens
Year: 2023
Published in: arXiv preprint arXiv:2306.06826, 2023 - arxiv.org
Institution: University of Michigan, University of Toronto
Research Area: Natural Language Processing
Discipline: Natural Language Processing, Human-Computer Interaction
DOI: https://doi.org/10.48550/arXiv.2306.06826
Citations: 55
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Authors: G Abercrombie, D Hovy
Year: 2023
Published in: 17th Linguistic ..., 2023 - researchportal.hw.ac.uk
Institution: Heriot Watt University
Research Area: Hate Speech Annotation, Computational Linguistics, Natural Language Processing, Annotation
Discipline: Computational Linguistics
DOI: https://doi.org/10.18653/v1/2023.law-1.10
Citations: 23
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Authors: LE Ruis, A Khan, S Biderman, S Hooker, T Rocktäschel
Year: 2022
Published in: 2022 - openreview.net
Institution: MILA, University of Toronto, Stanford University, Hugging Face, Imperial College London
Research Area: Natural Language Processing, Large Language Models, Communication
Discipline: Natural Language Processing
Citations: 52
