Language Models: Research Area — Prolific Citations Library

Discover 7 peer-reviewed studies in Language Models (2024–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 7 peer-reviewed papers in the research area of Language Models in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (7 of 7)

Bayesian teaching enables probabilistic reasoning in large language models

Authors: L Qiu, F Sha, K Allen, Y Kim, T Linzen, S van Steenkiste

Year: 2026

Published in: Nature …, 2026 - nature.com

Institution: Meta, Google DeepMind, Massachusetts Institute of Technology, Google Research, Google

Research Area: Probabilistic reasoning, Bayesian cognition, Neural language models, Reasoning, AI Evaluations

Discipline: Machine learning, Artificial intelligence

This paper sits at the intersection of machine learning and computational cognitive science, showing that large language models can acquire generalized probabilistic reasoning by being trained to imitate Bayesian belief updating rather than relying on prompting or heuristics.

Citations: 8
Visual cognition in multimodal large language models

Authors: LM Schulze Buschoff, E Akata, M Bethge

Year: 2025

Published in: Nature Machine ..., 2025 - nature.com

Institution: Max Planck Institute

Research Area: Visual Cognition, Multimodal Large Language Models (MLLMs), Vision-Language Models (VLMs)

Discipline: Cognitive Science, Artificial Intelligence, Computer Vision

Vision-based large language models show proficiency in visual data interpretation but fall short in human-like abilities for causal reasoning, intuitive physics, and social cognition.

Methods: Controlled experiments evaluating model performance on tasks related to intuitive physics, causal reasoning, and intuitive psychology using visual processing benchmarks.

Key Findings: Model capabilities in understanding physical interactions, causal relationships, and social preferences.

DOI: https://doi.org/10.1038/s42256-024-00963-y

Citations: 70
Towards Strategic Persuasion with Language Models

Authors: Z Cheng, J You

Year: 2025

Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org

Institution: University of Southern California, University of California Berkeley

Research Area: Artificial Intelligence, Computers and Society, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, LLM, RLHF

Discipline: Artificial Intelligence

This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.

Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.

Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.

Citations: 1
HRLAIF: Improvements in helpfulness and harmlessness in open-domain reinforcement learning from ai feedback

Authors: A Li, Q Xiao, P Cao, J Tang, Y Yuan, Z Zhao

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: Beijing University, Alibaba Group

Research Area: Reinforcement Learning from AI Feedback (RLAIF), Safety and Utility of Open-domain Language Models, Open Source LLM

Discipline: Artificial Intelligence

Citations: 12
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models

Authors: Thibaut Thonet, Jos Rozen, Laurent Besacier

Year: 2024

Published in: ArXiv

Institution: NAVER Labs

Research Area: Long-Context Language Models, Meeting Assistant Systems, Benchmark Evaluation

Discipline: Artificial Intelligence
Image-conditioned human language comprehension and psychometric benchmarking of visual language models

Authors: SN Pushpita, R Levy

Year: 2024

Published in: Proceedings of the 28th Conference on ..., 2024 - aclanthology.org

Institution: Masachusetts Institute of Technology

Research Area: Visual Language Models (VLMs), Psycholinguistics, Psychometric Benchmarking

Discipline: Artificial Intelligence

DOI: https://doi.org/10.18653/v1/2024.conll-1.34
MAIA: A benchmark for multimodal AI assessment

Authors: D Testa, G Bonetta, R Bernardi

Year: 2024

Published in: Proceedings of the ..., 2025 - aclanthology.org

Institution: Università di Roma La Sapienza, Fondazione Bruno Kessler, University of Pisa

Research Area: Multimodal AI Assessment, Visual Language Models (VLMs), Video Understanding, Computational Linguistics

Discipline: Artificial Intelligence, Computational Linguistics