Preprint / Version 1

A big team study evaluating translations with large language models

Comparisons between human and machine translations across 24 psychological measures

##article.authors##

DOI:

https://doi.org/10.51094/jxiv.2056

Keywords:

psychological scale, Large Language Models, machine translation, human translation, big team science, translation fidelity

Abstract

Large Language Models (LLMs) are widely used in many aspects of psychological research, but their applicability in translating psychological measures to match human translation remains unverified. This ManyScales project aims to evaluate the validity and applicability of LLM translations from multiple perspectives and compare them with human translations. This study is a large-scale collaborative endeavour within Japan (43 researchers, 36 institutions), focusing on 24 English-language psychological scales. For each scale, an LLM-translated version was developed using the R package LLMTranslate, alongside a human-translated version by professional researchers. Both versions are back-translated using a common procedure. Both translation versions will be compared from the viewpoint of (a) expert evaluation of semantic fidelity, naturalness, and cultural validity; (b) lay participants' assessment of understandability and naturalness; and (c) psychometric analysis (factor structure, factor scores, measurement invariance, some coefficients, etc.). Furthermore, we will explore cosine similarity based on embedded representations to examine semantic distance from the original items. This research aims to reveal the extent to which LLM translation can be used for psychometric scale translation and where differences from human translation manifest, thereby contributing to the visualization and standardization of the scale translation process.

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