  This paper describes the organization and findings of AXOLOTL'24, the first
multilingual explainable semantic change modeling shared task. We present new
sense-annotated diachronic semantic change datasets for Finnish and Russian
which were employed in the shared task, along with a surprise test-only German
dataset borrowed from an existing source. The setup of AXOLOTL'24 is new to the
semantic change modeling field, and involves subtasks of identifying unknown
(novel) senses and providing dictionary-like definitions to these senses. The
methods of the winning teams are described and compared, thus paving a path
towards explainability in computational approaches to historical change of
meaning.
