2 citations · 3 across the 11 of their papers we have counts for
10 papers · 1 filter
The Two-Process Theory of Machine Self-Report
Hubert Plisiecki, Filip Chmielewski, Kacper Dudzic +3
Language models are increasingly asked to self-report, informing safety evaluations, public understanding, and model-welfare debates. Yet their reports are elicited with human ques…
Supervised Semantic Differential for Cross-Cultural Concept Analysis: A Case Study of Human Affect
Jan Sikora, Paweł Lenartowicz, Hubert Plisiecki
Cross-cultural comparison of psychological meaning requires methods that go beyond word-level translation and examine how semantic dimensions are organized across languages. We int…
Semantic Gradients Interactions in SSD: A Case Study in Racial Identity and Hate Speech
Felix Ostrowicki, Hubert Plisiecki
We introduce interaction SSD, an extension of Supervised Semantic Differential that models how semantic meaning varies across moderators such as groups, traits, or conditions makin…
Psychological Constructs in Shared Semantic Space
Hubert Plisiecki
Psychological constructs are often measured in separate instruments, datasets, and research traditions, which makes direct comparison difficult. This paper proposes a framework for…
The Shared Latent Structure of LLM Psychometric Responses
Hubert Plisiecki, Sabina Siudaj, Kacper Dudzic +4
Human psychometric questionnaires are increasingly used to characterize large language models (LLMs), but it remains unclear what exactly their responses reflect. To answer this qu…
Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse
Hubert Plisiecki, Maria Leniarska, Jan Piotrowski +1
Supervised Semantic Differential (SSD) is a mixed quantitative-interpretive method that models how text meaning varies with continuous individual-difference variables by estimating…
The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instruments
Hubert Plisiecki
Text embeddings have become central to computational social science and psychology, enabling scalable measurement of meaning and mixed-method inference. Yet most representation lea…
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks
Hubert Plisiecki
This paper introduces the Semantic Propagation Graph Neural Network (SProp GNN), a machine learning sentiment analysis (SA) architecture that relies exclusively on syntactic struct…
Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-Poor Language
Hubert Plisiecki, Piotr Koc, Maria Flakus +1
This study explores the use of large language models (LLMs) to predict emotion intensity in Polish political texts, a resource-poor language context. The research compares the perf…
High Risk of Political Bias in Black Box Emotion Inference Models
Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus +1
This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA) in social science research. Machine learning models often reflec…