Exploring the In-context Learning Ability of Large Language Model for
  Biomedical Concept Linking
Qinyong Wang Zhenxiang Gao Rong Xu
The biomedical field relies heavily on concept linking in various areas such
as literature mining, graph alignment, information retrieval,
question-answering, data, and knowledge integration. Although large language
models (LLMs) have made significant strides in many natural language processing
tasks, their effectiveness in biomedical concept mapping is yet to be fully
explored. This research investigates a method that exploits the in-context
learning (ICL) capabilities of large models for biomedical concept linking. The
proposed approach adopts a two-stage retrieve-and-rank framework. Initially,
biomedical concepts are embedded using language models, and then embedding
similarity is utilized to retrieve the top candidates. These candidates'
contextual information is subsequently incorporated into the prompt and
processed by a large language model to re-rank the concepts. This approach
achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7%
in chemical entity normalization, exhibiting a competitive performance relative
to supervised learning methods. Further, it showed a significant improvement,
with an over 20-point absolute increase in F1 score on an oncology matching
dataset. Extensive qualitative assessments were conducted, and the benefits and
potential shortcomings of using large language models within the biomedical
domain were discussed. were discussed.