Text descriptions of reaction records from the test set of USPTO-ORD-100K are passed to the fine-tuned LLaMA-2-7B to generate structured data as text completions for model evaluation. Because a Reaction message consists of nested sub-messages (or "objects" in JSON terminology), such as Compound and ReactionWorkup, we can define evaluation tasks based on the comparison between the ground truth and LLM-inferred Reaction at the message level: Evaluation Metric 1. For a given message type, how many messages of this message type are accurately extracted or erroneously added, removed, or altered? Figure shows an example of Evaluation Metric 1 when comparing two ReactionInput messages given the message type of Compound messages. To distinguish the three failure modes, we first define a distance function for the given message type based on DeepDistance, an edit distance similar to Levenshtein distance designed for nested objects. When comparing two lists of messages (the shorter list is padded with empty messages such that two lists are of equal sizes), a bijective mapping between messages from two lists is found by minimizing the distance sum of all pairs, which is then used to identify the aforementioned failure modes.