T Li: Researcher — Prolific Citations Library

Explore 4 peer-reviewed studies by T Li in Probabilistic reasoning and Bayesian cognition (2024–2026). Discover research powered by Prolific's participant panel.

This page lists 4 peer-reviewed papers authored or co-authored by T Li in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

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
The inadequacy of reinforcement learning from human feedback - radicalizing large language models via semantic vulnerabilities

Authors: TR McIntosh, T Susnjak, T Liu, P Watters

Year: 2024

Published in: ... on Cognitive and ..., 2024 - ieeexplore.ieee.org

Institution: Cyberoo, Massey University, Cyberstronomy, RMIT University

Research Area: Semantic Vulnerabilities in LLMs, Ideological Manipulation, Reinforcement Learning from Human Feedback (RLHF) Limitations

Discipline: Computer Science, Artificial Intelligence, Machine Learning

RLHF mechanisms are insufficient to prevent semantic manipulation of LLMs, allowing them to express extreme ideological viewpoints when subjected to targeted conditioning techniques.

Methods: Psychological semantic conditioning techniques were applied to assess the susceptibility of LLMs to ideological manipulation.

Key Findings: The ability of LLMs to resist or adopt extreme ideological viewpoints under semantic conditioning.

Citations: 219
ImagenHub: Standardizing the evaluation of conditional image generation models

Authors: M Ku, T Li, K Zhang, Y Lu, X Fu, W Zhuang

Year: 2024

Published in: - arXiv preprint arXiv …, 2023 - arxiv.org

Institution: University of Waterloo, Ohio State University, University of California Santa Barbara, University of Pensylvania

Research Area: AI alignment, Representation learning, Cognitive computational modeling, Vision foundation models evaluation, Multimodal, Vision models

Discipline: Computer Science, Artificial Intelligence, Machine Learning

This paper presents a method for **aligning machine vision model representations with human visual similarity judgments across different abstraction levels, improving how well models reflect human perceptual and conceptual organization and enhancing generalization and uncertainty prediction.

DOI: https://doi.org/10.48550/arXiv.2310.01596

Citations: 59
Visualizing threat and trustworthiness prior beliefs in face perception in high versus low paranoia

Authors: A Bott, H Steer, J Faße, T Lincoln

Year: 2024

Published in: Nature

Institution: Universität Hamburg

Research Area: Face perception, Paranoia, Threat prior beliefs, Predictive Processing in Psychology.

Discipline: Psychology, Cognitive Science, Behavioral Science