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Found 8 papers, 1 papers with code
The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions
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
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
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.
Simulating Opinion Dynamics with Networks of LLM-based Agents
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation.
Learning interactions to boost human creativity with bandits and GPT-4
This paper considers how interactions with AI algorithms can boost human creative thought.
Decoding Emotional Experiences in Dyadic Conversations of Married Couples: Leveraging Semantic Similarity through Sentence Embedding
Results show that lower similarity is associated with greater positive emotional experiences in the positive (but not conflict) conversation.
Tutorials on Stance Detection using Pre-trained Language Models: Fine-tuning BERT and Prompting Large Language Models
This paper presents two self-contained tutorials on stance detection in Twitter data using BERT fine-tuning and prompting large language models (LLMs).
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states.
