The datasets that were used for training the NLP models are based on the "Chemical reactions from US patents (1976-Sep2016)" dataset 20 . This dataset contains experimental descriptions that were automatically extracted from publicly available US patents and patent applications in the given period. The style of writing is very similar to the style of experimental procedures published in the experimental parts of scientific articles, especially in organic chemistry journals. In a first approach, the dataset was annotated using ChemicalTagger , which uses a rule-based part-of-speech (POS) tagging algorithm. The POS-tagged experimental procedures were further processed into action graphs using a python script that traverses the xml files generated by ChemicalTagger, parses them for ActionPhrases, Molecules, and their associated Quantities, and combines them in a (linear) action graph. Procedures with only one ActionPhrase (usually <SYNTHESIZE> or <YIELD>) were excluded. This resulted in an annotated dataset with 1,573,734 entries consisting of experimental procedures and the corresponding action graphs, named dataset_chemtagger_raw. The dataset was further cleaned by replacing commonly occurring non-ASCII characters such as µ or × with u and x, removing extra line-breaks, leading and trailing spaces, and grouping actions in cases where the same action tags occurred consecutively, giving a new In a second approach, the recently published Llama-3.1-8B-Instruct 19 model was used for creating the annotated training data using in-context learning (ICL). The Llama v3.1 community license explicitly allows to use the outputs of the model for synthetic data generation and distillation. While the model weights are made publicly available and can be downloaded and used after a free sign-up process, hosting the model offline and using it for inference is still very resource intensive. Even the smallest model of the family with 8 billion parameters requires at least 16 GB of GPU RAM in its highest precision (fp16) just for the model weights, plus the memory that is needed for keeping the keys and values of each token in memory to leverage the models long available context length. This already exceeds the memory of most consumer-grade GPUs currently on the market. Moreover, the average inference time for creating an action graph of a single experimental procedure is still long, 24 seconds when run on a high-end GPU (A100-80GB), 352 seconds when run on a high-end CPU, or 29'109 seconds (8 hours) when run on a standard office laptop, as compared to 13, 36 and 316 seconds for a BigBirdPegasus-large model and 2, 5, and 70 seconds for a LongformerEncoderDecoder-base model (see Figure .b). Additionally, while the generated action graphs were generally of good quality, the model used its own action tags in 194'073 annotations (12.3%), even when being clearly instructed as part of its ICL prompt to only use tags from a pre-defined list of action tags (see SI for details). Overall, 2'956 different tags were used by the model in the annotations, instead of the 23 tags the model was instructed to use. Having such a large number of (unexpected) tags would make the generated action graphs very specific and also render the automatic downstream processing of action graphs much more complicated. This further corroborates the efforts for training and using a much smaller, domain-specific surrogate model for the specific task of action graph generation, rather than querying a more general, larger LLM using ICL. More details about the (semi-)automatic annotation of the dataset with the help of the Llama-3.1-8B-Instruct and Llama-3.1-70B-Instruct models as well as manually performed substitutions for data cleanup can be found in the SI. The final dataset (dataset_llama_cleaned) that was used for training the surrogate models contained 26 different tags (the "original" 21 ChemicalTagger action tags, plus the newly added tags <MIX>, <ADJUSTPH>, <REPEAT>, <SONICATE>, and <CENTRIFUGE>), occurring with frequencies varying between 2'700'427 for the most common action <ADD> to 1'453 for the least common action <CENTRIFUGE> (see Figure .a).