  Entity Matching is the task of deciding whether two entity descriptions refer
to the same real-world entity and is a central step in most data integration
pipelines. Many state-of-the-art entity matching methods rely on pre-trained
language models (PLMs) such as BERT or RoBERTa. Two major drawbacks of these
models for entity matching are that (i) the models require significant amounts
of task-specific training data and (ii) the fine-tuned models are not robust
concerning out-of-distribution entities. This paper investigates using
generative large language models (LLMs) as a less task-specific training
data-dependent and more robust alternative to PLM-based matchers. Our study
covers hosted and open-source LLMs, which can be run locally. We evaluate these
models in a zero-shot scenario and a scenario where task-specific training data
is available. We compare different prompt designs and the prompt sensitivity of
the models and show that there is no single best prompt but needs to be tuned
for each model/dataset combination. We further investigate (i) the selection of
in-context demonstrations, (ii) the generation of matching rules, as well as
(iii) fine-tuning a hosted LLM using the same pool of training data. Our
experiments show that the best LLMs require no or only a few training examples
to perform similarly to PLMs that were fine-tuned using thousands of examples.
LLM-based matchers further exhibit higher robustness to unseen entities. We
show that GPT4 can generate structured explanations for matching decisions. The
model can automatically identify potential causes of matching errors by
analyzing explanations of wrong decisions. We demonstrate that the model can
generate meaningful textual descriptions of the identified error classes, which
can help data engineers improve entity matching pipelines.
