SIREN: A Python Package for Automated Psychometric Scale Shortening Using Document Embeddings
Related Experiment Video
Updated: Nov 1, 2026

Fine-Tuning Large Language Models Using Entity Hallucination Index for Text Summarization
Published on: January 9, 2026
Benjamin Listyg1, Brennah V Ross2
1Department of Psychology, University of Georgia, Athens, GA, USA.
Abstract:
SIREN (Semantic Item Reduction Engine) is an open-source Python package that shortens unidimensional and multidimensional scales from item text alone, without respondent data. Items are encoded as transformer-based embeddings, and scale reduction is framed as constrained optimization over the resulting similarity matrix: retained items maximize within-dimension coherence or coverage while minimizing between-dimension similarity, subject to user-specified item counts per dimension. Ant colony optimization is the default search, with SLSQP, genetic algorithm, and simulated annealing alternatives. Diagnostic functions report coverage of dropped items, benchmark any short form against random subsets, and flag redundant items. SIREN runs on Windows, Linux, and macOS and is installable from PyPI.
Related Concept Videos
Self-Report Tests of Personality
Scaling
Introduction to z Scores
z scores help...
Introduction to z Scores
z scores help...
Sensory Modalities
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...