Notably, the relative performance difference between the validation (in-distribution) and test (out-of-distribution) evaluations is more pronounced for our ICL approach. This indicates that foundation models, even though often presumed to generalize well, heavily depend on relevant demonstrations in such domainspecific settings assuming no model optimization toward the target domain is performed. The performance difference between both domains could originate from the domain gap, i.e. distinct input and/or label distributions. While the validation dataset has neither domain shift nor distinct author style with respect to the training dataset, both differences exist for the test dataset. In the current state, it is difficult to assess the quantitative contributions to performance through the (i) limited corpus, (ii) the quality of the retrieved demonstrations, (iii) and generation, i.e. transfer of the relevant information from the demonstrations to the target sentence by the LLM. Intuitively, retrieval of demonstrations from the training dataset should be easier for the validation case due to matching domain and style. As a quantitative metric for retrieval quality, the Euclidean distance between embedding vectors representing the target sentence and retrieved demonstrations can be used. In our case, the average Euclidean distances amounted to 0.347 and 0.333 for target sentences from test and validation, respectively. This marginal difference might be owed to the usage of an all-purpose embedding model which squashes content from materials mechanics into a smaller vector subspace without taking the distinct subdomain between test and validation into account sufficiently. Similarly, it is to be expected that the LLM can infer entities easier if the provided demonstrations originate from the same corpus. Increasing the base corpus in variance and an improved retrieval-strategy alongside domain-specific embedding models which retrieve semantically similar instances and are style-agnostic can facilitate a performance improvement.