Bi-encoder architectures for biomedical entity linking learn flat embedding spaces that ignore ontology structure. HiCo-EL encodes hierarchical knowledge into bi-encoder representations through hierarchical contrastive pretraining. The core idea is within-space hierarchical multi-label contrastive learning (HMLC): contrastive losses applied independently within each encoder space, preserving cross-space compatibility for retrieval. Our formulation drops the monotonicity constraints of prior HMLC work, handles variable-depth hierarchy paths, and adds depth-weighted alignment between encoder spaces. Treating each entity as a leaf node unifies coarse type structure with fine-grained entity discrimination. On MedMentions and NCBI Disease, HiCo-EL outperforms ArboEL by +3.7% and +1.8% Recall@1 respectively. The gains reveal a key insight: breadth amplifies, depth generalizes—broad hierarchies help common entities most (+6.3% R@1), while deep hierarchies benefit unseen entities (+9.6% R@1 zero-shot).

Biomedical Named Entity Recognition (NER) consists of identifying and classifying important biomedical entities mentioned in text. Traditionally, biomedical NER has heavily relied on domain-specific pre-trained language models; particularly variant of BERT models. With the emergence of large language models (LLMs), some studies have evaluated their performance on biomedical NLP tasks. These studies consistently show that, despite their general capabilities, LLMs still fall short compared to specialized BERT-based models for biomedical NER. However, as LLMs continue to advance at a remarkable pace, natural questions arise: Are they still far behind, or are they starting to be competitive? In this study, we investigate the performance of recent LLMs across multiple biomedical NER datasets under both clean and noisy dataset conditions. Our findings reveal that LLMs are progressively closing the performance gap with BERT-based models and demonstrate particular strengths in low-data settings. Moreover, our results suggest that in-context learning with LLMs exhibits a notable degree of robustness to noise, making them a promising alternative in settings where labeled data is scarce or noisy.

Scientific abstracts and lay summaries serve distinct but critical roles in research communication. Abstracts use technical language for academic audiences, while lay summaries aim to make findings accessible to non-specialists. With the rise of large language models (LLMs), there is increasing interest in automating the generation of both types of summaries—especially in the biomedical domain, where clarity and factual accuracy are essential. This study evaluates the performance of lightweight LLMs (under 8B parameters) in generating biomedical abstracts and lay summaries in a zero-shot setting. We assess outputs across three key dimensions: relevance, readability, and factuality. Additionally, we introduce a novel analysis of the sectional origin and desirability of information—where desirability reflects the utility of content from the reader’s perspective. We further compare human and LLM preferences using an objective ranking task. Our results show that LLM-generated summaries often contain comparable levels of desirable information to gold-standard human references. In several cases, LLM outputs are preferred by human evaluators and occasionally mistaken for human-authored text. These findings demonstrate the potential of lightweight LLMs for scalable, high-quality summarization and suggest their practical use in domains requiring both technical and accessible communication. The codebase for this study is publicly available on GitHub: https://github.com/batuinmetz/Understanding-LLMs-summarization-capabilities

Entity linking involves normalizing a mention in medical text to a unique identifier in a knowledge base, such as UMLS or MeSH. Most entity linkers follow a two-stage process: first, a candidate generation step selects high-quality candidates, and then a named entity disambiguation phase determines the best candidate for final linking. This study demonstrates that leveraging a large language model (LLM) as an entity disambiguator significantly enhances entity linking models’ accuracy and recall. Specifically, the LLM disambiguator achieves remarkable improvements when applied to alias-matching entity linking methods. Without any fine-tuning, our approach establishes a new state-of-the-art (SOTA), surpassing previous methods on multiple prevalent biomedical datasets by up to 16 points in accuracy. We released our code on GitHub at https://github.com/ChristopheYe/llm_disambiguator