  Entity matching is the task of linking records from different sources that
refer to the same real-world entity. Past work has primarily treated entity
linking as a standard supervised learning problem. However, supervised entity
matching models often do not generalize well to new data, and collecting
exhaustive labeled training data is often cost prohibitive. Further, recent
efforts have adopted LLMs for this task in few/zero-shot settings, exploiting
their general knowledge. But LLMs are prohibitively expensive for performing
inference at scale for real-world entity matching tasks.
  As an efficient alternative, we re-cast entity matching as a conditional
generation task as opposed to binary classification. This enables us to
"distill" LLM reasoning into smaller entity matching models via natural
language explanations. This approach achieves strong performance, especially on
out-of-domain generalization tests (10.85% F-1) where standalone generative
methods struggle. We perform ablations that highlight the importance of
explanations, both for performance and model robustness.
