Search Results for author: Timothy T. Rogers

Found 10 papers, 0 papers with code

The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions

no code implementations21 Feb 2024 Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome.

The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents

no code implementations16 Nov 2023 Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka, Agam Goyal, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers

Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds."

Evolving Domain Adaptation of Pretrained Language Models for Text Classification

no code implementations16 Nov 2023 Yun-Shiuan Chuang, Yi Wu, Dhruv Gupta, Rheeya Uppaal, Ananya Kumar, Luhang Sun, Makesh Narsimhan Sreedhar, Sijia Yang, Timothy T. Rogers, Junjie Hu

Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection.

Domain Adaptation Stance Detection +3

Simulating Opinion Dynamics with Networks of LLM-based Agents

no code implementations16 Nov 2023 Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation.

Misinformation Prompt Engineering

Semantic Feature Verification in FLAN-T5

no code implementations12 Apr 2023 Siddharth Suresh, Kushin Mukherjee, Timothy T. Rogers

This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science.

Language Modelling Large Language Model +2

Human-machine cooperation for semantic feature listing

no code implementations11 Apr 2023 Kushin Mukherjee, Siddharth Suresh, Timothy T. Rogers

Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor.

How does task structure shape representations in deep neural networks?

no code implementations NeurIPS Workshop SVRHM 2020 Kushin Mukherjee, Timothy T. Rogers

While modern deep convolutional neural networks can be trained to perform at human levels of object recognition and learn visual features in the process, humans use vision for a host of tasks beyond object recognition including — drawing, acting, and making propositional statements.

Object Recognition

Human Memory Search as Initial-Visit Emitting Random Walk

no code implementations NeurIPS 2015 Kwang-Sung Jun, Jerry Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan

In this paper, we propose the first efficient maximum likelihood estimate (MLE) for INVITE by decomposing the censored output into a series of absorbing random walks.

Human Rademacher Complexity

no code implementations NeurIPS 2009 Jerry Zhu, Bryan R. Gibson, Timothy T. Rogers

We propose to use Rademacher complexity, originally developed in computational learning theory, as a measure of human learning capacity.

Generalization Bounds Learning Theory

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