Can Large Language Models Advance Crosswalks? The Case of D anish Occupation Codes (2025)

Abstract: 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.

## introduction crosswalks are structured mappings that connect one classification system to another, enabling data to be compared or integrated across different contexts. these mappings are essential in numerous domains, from harmonizing occupational codes across time or countries @xcite to aligning taxonomies in biology @xcite or mapping educational milestones between frameworks @xcite . while the contexts vary, the underlying challenge remains the can llms do the job? how well? figure 1 : an example of crosswalks between two codebooks from the danish occupation data. translations are in commas. traditionally, crosswalks are created manually by humans. can llms assist in this process? same: translating between systems that often reflect different conceptual frameworks, levels of granularity, or terminologies. in the context of occupational classifications, for instance, crosswalks allow researchers to analyze labor market trends across time or national boundaries despite differences in coding systems. figure 1 gives an example of crosswalks based on danish occupation data. however, creating these mappings is a complex and labor-intensive process @xcite . large language models (llms) offer a promising avenue for addressing this challenge. yet, their use in creating crosswalks raises essential questions: how can llms reliably infer mappings between systems with limited contextual overlap? what are the best strategies for prompting llms to elicit meaningful, interpretable outputs? and how do we ensure that the outputs of llms align with domain-specific requirements while remaining accessible to human users? this paper explores the potential of llms to assist in creating crosswalks, using danish occupational classifications from two different time points as a case study @xcite . our aim is not to fully automate crosswalk creation but to develop an assisted workflow that combines the efficiency of llms with the judgment of human experts. using a curated two-round judgment framework, we compare the performance of different llms to evaluate their strengths and limitations in supporting this task. our empirical findings indicate that, despite certain limitations, the interactive llm-based crosswalking process outperforms an embedding-based baseline. through this work, we contribute to the growing field of nlp applications in social science research, showing how llms can be effectively integrated into complex domain-specific knowledge-mapping tasks. ## background much of the work at the intersection of nlp and computational social science (css) focuses on labeling texts from social science domains to systematically analyze patterns, opinions, or topics @xcite . occupational coding, a critical task in labor market research and social science, is an excellent use case to explore if and how large language models can enhance methodological approaches in these fields @xcite @xcite . occupational codes are standardized labels assigned to jobs based on their duties, responsibilities, and required skills. however, occupational coding is a particularly complicated task because job descriptions can be context-dependent, and often ambiguous @xcite . adding to this complexity, different countries and time periods often use distinct occupational classification schemes, each tailored to specific economic, social, or policy contexts. for instance, the international standard classification of occupations (ilo, 2025) may differ significantly from national systems like the u.s. standard occupational classification (bls, 2025), necessitating the development of crosswalks to translate codes from one system to another. crosswalks like the one by @xcite establish equivalencies between two occupational classification schemes allowing data coded in one system or country (us vs. canada) to be translated into another. this process is essential for enabling international comparisons, historical analyses, and the integration of datasets that rely on different coding standards. these crosswalks are typically created manually by domain experts who possess deep knowledge of the classification schemes in question. for example, humlum (2021) developed a detailed crosswalk for denmark's disco classifications. while such manually created crosswalks are highly accurate and tailored to specific needs, they are also exceptionally time-intensive and resource-intensive to produce, as they require establishing mappings between several hundred codes in each classification scheme. therefore, there is growing interest in exploring whether llms can assist in the creation of crosswalks. similar efforts have been made in other domains, such as healthcare and biomedical research, where tools like mappergpt use large language models to refine and align entity mappings @xcite . ## method we propose a two-round prompt-based framework to conduct the crosswalks for the occupation codes. the basic idea of the crosswalk is to find the possible matching code from codebook b for every code in codebook a. figure 2 illustrates the basic workflow of our framework. the first round is about prompting the models to do similarity checks with certain degrees across all codes in both codebooks. based on the results from the first round, the second round is about selecting the final candidate matching code from another codebook. this search is done for every code in one of the codebooks. the workflow is detailed as follows: round 1: similarity check across codes. we begin with two codebooks (a and b) to work on, where codebook a contains a codeset of unique code names (code a 1, code a 2, ...), and codebook b contains a codeset of unique code names (code b 1, code b 2, ...). the task of the crosswalk is to map the codes from a to the codes from b. therefore, in the initial step, we construct code pairs for each code from codebook a to every code from codebook b. prompting the model with similarity options a: extremely similar extracting model output results for all code pairs codeb 1 codeb 2 codeb 3 codea 1 a a d codea 2 c e b codea 3 d a b selecting candidate codes for the given source occupation code, please select one matching occupation from the given candidate occupation codes. source occupation code: code a 1 candidate occupation code(s): a: codeb 1 b: codeb 2 prompting the model for candidate selection a: codeb 1 extracting model output round 1: similarity check across codes round 2: candidate code selection for each code pair, we prompt the llm with a question asking for the similarity and options indicating different similarity polarities at 5 scales (from a to e indicating extremely similar towards not similar at all). this scale is commonly used for survey questionnaires, due to its structured design, which presents respondents with predefined answer options, reducing ambiguity and ensuring consistency in responses; as well as its format, which facilitates faster decision-making by guiding participants through a clear set of choices, minimizing cognitive load and improving response accuracy @xcite . this setup has also been recently increasingly introduced in llm evaluation, to assess the opinions, knowledge, and behaviors embedded in llm models (e.g., @xcite @xcite @xcite . the response of the llm is then extracted using a string matching method using regex to map the responses to the 5 scale points. after all code pairs have been evaluated, we save the results in a table representing the similarities between the codes in matrix format. round 2: candidate code selection. with the similarity results for all code pairs collected from the first round, the task of the second round is to find one final code partner for each code of codebook a. as the results from the first round are distributed across the five scale points a-e, we se-lect the potential code matches by taking the codes rated with "a. extremely similar" or "b. very similar" to be the candidates for final selection. in case there are no a or b results, we consider that this source code does not have a matching code in codebook b. we then prompt the llm with the source code from codebook a and the candidate codes from codebook b (i.e., those that have a similarity result of "extremely similar" or "very similar"). we ask the llm to select the code from the candidate codes b that matches a best, and extract the model output. in the end, we construct the final codebook for the mapped code pairs. ## experimental setups data -the danish occupation codes. we use the 6-digit, level 5 granularity of disco-loen foot_0 88 and 08 from statistics denmark as codebook a and b respectively to test our framework. it is standard practice for crosswalks to be produced at the most granular level of a hierarchical code system to utilize the specificity of description. mapping code pairs at lower levels of granularities can be aggregated to produce associations between codes in higher-level granularities (e.g. level 5 to level 4), but the reverse is not true. ## results main results. mistral, however, achieved an f1 score of only 40.58%, showing limited effectiveness in this specific application. this result may reflect differences in the architecture or training data of the model, which could make it less suited for nuanced crosswalk mapping tasks in danish. the multilingual embedding model baseline attained an f1 score of 57.12%, performing better than mistral but falling short of the other three instruction-tuned llms. these results highlight the advantages of instruction-tuned models for complex semantic tasks compared to traditional embedding-based methods. ## discussion & conclusion the results of this study demonstrate the potential of llms to assist in creating crosswalks for occupational classifications. our findings highlight the advantages of instruction-tuned llms in handling semantic complexity and improving efficiency compared to traditional embedding-based approaches. models like qwen2.5 showed strong performance in aligning danish occupational codes, emphasizing the value of instruction tuning and contextual understanding in these tasks. however, the relatively low inter-model agreement underscores the variability in outputs across different llms, pointing to the importance of model selection and parameter tuning. this variability also highlights the need for integrating human expertise into the workflow to validate and refine llm-generated mappings. the interactive, prompt-based framework we proposed aligns with the concept of human-in-the-loop workflows, where llms augment rather than replace expert judgment. additionally, our findings highlight the advan-tages of maintaining model consistency across rounds, especially for strong models like qwen2.5. swapping models, particularly when involving weaker ones like mistral, leads to diminished results, emphasizing the need for robust and consistent modeling strategies. our findings also resonate with similar efforts in other domains, such as mappergpt, which refines entity mappings in fields like healthcare and biomedical research @xcite . these parallels reinforce the versatility of llms in supporting knowledge-mapping tasks across diverse contexts, though domain-specific adaptations remain critical for success. future work could explore how our two-step prompting framework can be extended beyond occupational classifications to other classification mapping tasks in fields such as finance, education, and public administration, where structured yet flexible mappings are essential for accurate data integration and interoperability.