Natural Language Processing Nlp: Academic Discipline — Prolific Citations Library

Explore 4 peer-reviewed papers in Natural Language Processing Nlp (2021–2026). Academic research using Prolific for high-quality human data collection.

This page lists 4 peer-reviewed papers in the discipline of Natural Language Processing Nlp in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs

Authors: C Yuan, B Ma, Z Zhang, B Prenkaj, F Kreuter, G Kasneci

Year: 2026

Published in: arXiv preprint arXiv:2601.08634, 2026•arxiv.org

Institution: Munich Center for Machine Learning, LMU Munich, Technical University of Munich

Research Area: Artificial Intelligence, AI Ethics, AI Alignment, Political Science, Computational Social Science

Discipline: Computer Science, Natural Language Processing (NLP)

This paper examines how large language models’ (LLMs) political outputs shift when you explicitly prime them with different moral values. Instead of just assigning fake personas (like “pretend to be liberal”), the authors condition models to endorse or reject specific moral values (e.g., utilitarianism, fairness, authority). They then measure how those moral primes move the models’ positions in...

DOI: https://doi.org/10.48550/arXiv.2601.08634
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models

Authors: L Ibrahim, C Akbulut, R Elasmar, C Rastogi, M Kahng, MR Morris, KR McKee, V Rieser, M Shanahan, L Weidinger

Year: 2025

Published in: arXiv preprint arXiv:2502.07077, 2025•arxiv.org

Institution: Google DeepMind, Google, University of Oxford

Research Area: Multimodal conversational AI, conversational AI, Evaluation methodology, benchmarking

Discipline: Computer Science, Natural Language Processing (NLP), Human–Computer Interaction (HCI)

The paper evaluates anthropomorphic behaviors in SOTA LLMs through a multi-turn methodology, showing that such behaviors, including empathy and relationship-building, predominantly emerge after multiple interactions and influence user perceptions.

Methods: Multi-turn evaluation of 14 anthropomorphic behaviors using simulations of user interactions, validated by a large-scale human subject study.

Key Findings: Anthropomorphic behaviors in large language models, including relationship-building and pronoun usage, and their perception by users.

Citations: 26

Sample Size: 1101
Order Effects in Annotation Tasks: Further Evidence of Annotation Sensitivity

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 (NLP), Computational Social Science
Exploring cross-cultural differences in English hate speech annotations: From dataset construction to analysis

Authors: N Lee, C Jung, J Myung, J Jin

Year: 2021

Published in: Proceedings of the ..., 2024 - aclanthology.org

Institution: KAIST, Cardiff University

Research Area: Hate Speech Annotation, Cross-Cultural Bias, NLP Ethics

Discipline: Natural Language Processing (NLP), Computational Social Science

Citations: 44