On the Limits of Candidate Generation in Biomedical Entity Linking

Biomedical entity linking systems rely on candidate generation to retrieve potential entities prior to disambiguation. This stage imposes a hard upper bound on end-to-end performance: if the correct entity is not retrieved, no downstream model can recover it. Despite this, candidate generation is typically treated as a standalone retrieval problem, with limited attention to its structural limitations. In this work, we argue that candidate generation is inherently incomplete under any single retrieval paradigm and that improving coverage requires exploiting complementary retrieval behaviors. We introduce a heterogeneous ensemble framework that combines diverse candidate generators and analyze how their differences contribute to increased coverage. Empirically, our approach substantially improves the inclusion of gold entities in candidate sets across multiple biomedical benchmarks. When paired with standard disambiguation models, including cross-encoders and LLM-based rerankers, these gains translate into consistent end-to-end improvements, with an average increase of 8.1 absolute points across all datasets with our best disambiguator. We further show that diversity—not just individual model strength—is an important contributor to these gains, and we quantify the diminishing returns of adding additional generators. Finally, we demonstrate that a simple heuristic for aggregating candidates achieves near-optimal performance relative to an oracle, suggesting that most gains arise from coverage expansion rather than complex fusion strategies. Our results highlight candidate generation as a fundamental bottleneck in entity linking and provide a principled approach to mitigating it.