Crosswalks, which map one classification system to another, are critical tools for harmonizing data across time, countries, or frameworks. However, constructing crosswalks is labor-intensive and often requires domain expertise. This paper investigates the potential of Large Language Models (LLMs) to assist in creating crosswalks, focusing on two Danish occupational classification systems from different time periods as a case study. We propose a two-stage, prompt-based framework for this task, where LLMs perform similarity assessments between classification codes and identify final mappings through a guided decision process. Using four instruction-tuned LLMs and comparing them against an embedding-based baseline, we evaluate the performance of different models in crosswalks. Our results highlight the strengths of LLMs in crosswalk creation compared to the embedding-based baseline, showing the effectiveness of the interactive prompt-based framework for conducting crosswalks by LLMs. Furthermore, we analyze the impact of model combinations across two interactive rounds, highlighting the importance of model selection and consistency. This work contributes to the growing field of NLP applications for domain-specific knowledge mapping and demonstrates the potential of LLMs in advancing crosswalk methodologies.

Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. These cognitive-behavioral traits include typically Attitudes, Opinions, Values (AOVs). However, measuring AOVs embedded within LLMs remains opaque, and different evaluation methods may yield different results. This has led to a lack of clarity on how different studies are related to each other and how they can be interpreted. This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs. Moreover, we survey related approaches in different stages of the evaluation pipeline in these works. By doing so, we address the potential and challenges with respect to understanding the model, human-AI alignment, and downstream application in social sciences. Finally, we provide practical insights into evaluation methods, model enhancement, and interdisciplinary collaboration, thereby contributing to the evolving landscape of evaluating AOVs in LLMs.