  Entities lie in the heart of biomedical natural language understanding, and
the biomedical entity linking (EL) task remains challenging due to the
fine-grained and diversiform concept names. Generative methods achieve
remarkable performances in general domain EL with less memory usage while
requiring expensive pre-training. Previous biomedical EL methods leverage
synonyms from knowledge bases (KB) which is not trivial to inject into a
generative method. In this work, we use a generative approach to model
biomedical EL and propose to inject synonyms knowledge in it. We propose
KB-guided pre-training by constructing synthetic samples with synonyms and
definitions from KB and require the model to recover concept names. We also
propose synonyms-aware fine-tuning to select concept names for training, and
propose decoder prompt and multi-synonyms constrained prefix tree for
inference. Our method achieves state-of-the-art results on several biomedical
EL tasks without candidate selection which displays the effectiveness of
proposed pre-training and fine-tuning strategies.
