Learning from Negative Samples in Biomedical Generative Entity Linking (2025)

Abstract: Generative models have become widely used in biomedical entity linking (BioEL) due to their excellent performance and efficient memory usage. However, these models are usually trained only with positive samples—entities that match the input mention’s identifier—and do not explicitly learn from hard negative samples, which are entities that look similar but have different meanings. To address this limitation, we introduce ANGEL (Learning from Negative Samples in Biomedical Generative Entity Linking), the first framework that trains generative BioEL models using negative samples. Specifically, a generative model is initially trained to generate positive entity names from the knowledge base for given input entities. Subsequently, both correct and incorrect outputs are gathered from the model’s top-k predictions. Finally, the model is updated to prioritize the correct predictions through preference optimization. Our models fine-tuned with ANGEL outperform the previous best baseline models by up to an average top-1 accuracy of 1.4% on five benchmarks. When incorporating our framework into pre-training, the performance improvement increases further to 1.7%, demonstrating its effectiveness in both the pre-training and fine-tuning stages. The code and model weights are available at https://github.com/dmis-lab/ANGEL.

## introduction biomedical entity linking (bioel) involves aligning entity mentions in text with standardized concepts from biomedical knowledge bases (kb) such as umls @xcite or mesh @xcite . 1 bioel encounters significant challenges due to the diverse and ambiguous nature of biomedical terminology, including synonyms, abbreviations, and terms that look similar but have different meanings. for instance, 'adhd' figure 1 : comparison of training approaches between existing generative bioel models and our angel method. the main limitation of current generative bioel methods is that they are trained only on positive samples. this restricts their ability to distinguish between entity names that are similar in surface form but different in meaning depending on the context. our an-gel framework addresses this issue by training the model to prefer positive samples over negative ones. recent studies have focused on addressing these challenges, broadly categorized into two approaches: similarity-based and generative bioel. similarity-based models @xcite @xcite encode input mentions and entities from kbs into the same vector space using embedding models. they then calculate similarity scores to identify the most similar entities for each input entity. although these approaches have achieved remarkable improvements, they require significant space to index and load embedding vectors for all candidate entities @xcite . furthermore, representing both the input and candidate entities as single vectors using a bi-encoder can limit the quality of their representations, making it difficult to handle challenging cases. on the other hand, generative models @xcite , built upon an encoder-decoder structure @xcite , directly generate the most likely entity name from the kb for the input entity. the output space is dynamically controlled through a constrained decoding strategy, ensuring that only entities from the target kb are generated. generative models offer several advantages over similaritybased models, including greater memory efficiency and higher performance. they eliminate the need to index large external embedding vectors, and their auto-regressive formulation effectively crossencodes the input document and candidate entities. however, existing generative models are trained solely on positive samples and do not explicitly learn from negative samples. despite their high performance, they encounter limitations when distinguishing between biomedical entities with similar surface forms but different meanings. although similarity-based models address this issue by incorporating negative samples through synonym marginalization @xcite or contrastive learning @xcite , applying these approaches to generative models is not straightforward. consequently, generative models may overfit to surface-level features, reducing the models' ability to generalize effectively across varied contexts, as illustrated in figure 1 . to harness the benefits of generative approaches while overcoming their limitation of not using negative samples, we introduce a novel training framework, angel. our framework operates in two stages: positive-only training and negative-aware training (see figure 2 ). in the first stage, a generative model is trained to generate biomedical terms from the kb that share the same identifier as the given input entity. in the second stage, we gather both correct and incorrect outputs from the model's top-k predictions. the model is then updated to prioritize the correct predictions using a preference optimization algorithm @xcite . models trained on our an-gel framework significantly outperform the previous best similarity-based and generative bioel models, achieving an average accuracy improvement of 1.7% across five datasets. our contributions are as follows: ## conclusions in this study, we discussed the importance of negative samples in training generative bioel models and introduced angel, the first framework in this field to effectively incorporate negative-aware training into a generative model. our models demonstrated the ability to learn subtle distinctions between entities with similar surface forms and contexts. experimental results showed that angel outperformed existing similarity-based and generative models, with notable performance improvements of 0.9%, 1.4%, and 1.7% for bart, bio-bart, and genbioel, respectively, achieving the best performance across five public bioel datasets.