---
title: "From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings — Stuff That Spins"
description: "arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating …"
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date: "2026-07-09T04:00:00+00:00"
modified: "2026-07-09T06:03:48.510177+00:00"
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# From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings

**Source:** Unknown  
**Published:** July 9, 2026  
**Original:** https://arxiv.org/abs/2607.07141  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating a cold start problem for item calibration. Predicting item parameters from features is a long standing measurement problem dating back to the Linear Logistic Test Model; modern text embeddings now automate the design matrices traditionally specified by hand. We propose an evaluation framework combining regularized regression on item text e

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