## 4 Method
### Large Language Model with Heuristic Classifier  
The LLM-based approach builds upon the GPT API to classify the automatability and offshorability of skills (Figure 1). LLMs based on GPT models are considerably more complex than Transformer models and harder to adapt to specific domains [38]. They are useful for in-context learning from prompts, especially in few-shot settings, but they are sometimes saddled with fairness issues (e.g., stereotypes, biases, errors) [46]. The LLM classifier described here queries a GPT model for assessments on a skill's basic characteristics as outlined by [PERSON] and [PERSON] [12]: _braun (B)_: physical interaction with objects is needed; _people (P)_: interactions with humans are required; _brain (T)_: abstract reasoning is needed; _location (L)_: the activity's location matters (e.g., customer's or own location); _digitalization (D)_: activity can be digitalized; _routine (R)_: activity can be standardized into subprocesses that are performed similarly around the world. We use OpenAI's _gpt-3.5-turbo_ model and a prompt that provides few-shot examples to contextualize the request--e.g., the next prompt helps us obtain a binary skill classification: \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Base Language Model**} & **Bert Base** & \multirow{2}{*}{**DistilBERT Base**} & \multirow{2}{*}{**XLM Roberta**} \\  & **Multilingual** & & \\ \hline Activation & Gelu & Gelu & Gelu \\ Attention dropout & 0.1 & 0.1 & 0.1 \\ Dimension & 768 & 3072 & 1024 \\ Dropout & 0.1 & 0.1 & 0.1 \\ Hidden layer dimensions & 12 & (n.a.) & 24 \\ Initialize range & 0.02 & 0.02 & 0.02 \\ Max position embeddings & 512 & 512 & 514 \\ Learning rate & AdamW & AdamW & AdamW \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model specification and hyperparameters  
Figure 1: Obtaining details on skill automatability and offshorability via GPT and heuristics. Can the activity _[experiment planning]_ necessarily only be carried out on-site at the customer's location? (answer only with Yes or No):  
Examples/Yes: _[production order monitoring]_, _[building maintenance]_, _[object cleaning]_, _[tunnel measuring]_.  
Examples/No: _[customer file updating]_, _[door producing]_, _[ensure analysis quality]_, _[coordinate program area]_. The example prompt requests an assessment of whether the task _experiment planning_ needs to be performed on-site and provides examples for skills with a positive (_monitor production order_, _maintain buildings_, _clean object_, and _measure tunnel_) and a negative (_update customer file_, _produce door_, _ensure analysis quality_, and _coordinate program area_) assessment. Each prompt is built around a single indicator from [PERSON] and [PERSON]'s list [12], as combining the indicators was found to lead to worse results. The indicators were extracted for each skill using the prompts and then used to calculate with a heuristic whether the skill is automatable or offshorable. We categorize a skill as automatable when the threshold value of 0.5 is surpassed for _automatable_ according to the equation below:  
\[automatable = 1-(0.4\cdot B+0.3\cdot P+0.2\cdot T+S-0.4\cdot D) \tag{1}\]  
In this equation, all parameters (\(B\), \(P\), \(T\), \(S\), and \(D\)) are binary variables, taking values of either 0 or 1. A value of 0 indicates the absence of the characteristic, while a value of 1 signifies its presence. \(B\) denotes the degree of physical interaction (brawn), \(P\) reflects the degree of interaction with people, \(T\) represents the level of abstract thinking required (brain), \(D\) indicates the extent to which the task can be performed digitally (digitalization), and \(L\) signifies the necessity of on-site presence (location). The weights (0.4, 0.3, 0.2, and \(-0.4\)) have been determined empirically. As outlined in Equation (2), a task is considered offshorable if it does not have to be performed on-site (location \(L=0\)) and can either be digitalized (digitalization \(D=1\)) or standardized (routine \(R=1\)).