Schema-Grounded Multitask Instruction Fine-tuning for Joint Biomedical Named Entity Recognition and Relation Extraction in Pharmacovigilance

Abstract

MotivationPharmacovigilance relies on accurate extraction of structured biomedical entities and their semantic relationships from scientific literature. However, most biomedical information extraction systems address named entity recognition (NER) and relation extraction as separate tasks trained on corpus-specific architectures, limiting scalability and cross-task knowledge sharing. Recent developments in instruction-tuned Large Language Models (LLMs) offer a promising alternative through unified generative extraction, but robust schema-grounded multitask adaptation for biomedical extraction is still understudied. MethodsThis study proposes a unified multitask instruction-tuned LLM framework that jointly performs biomedical NER and relation extraction across three benchmark corpora to identify chemical, disease, drug entities, as well as chemical-disease relations, drug-adverse event relations, and drug-drug interactions. Two general LLMs, Llama-3.2-3B-Instruct and Qwen3-8B, were fine-tuned using Low-Rank Adaptation (LoRA) under a shared generation interface that extracts both entity pairs and their underlying relation. Zero-shot and fine-tuned configurations were evaluated across all the tasks on their respective held-out test sets. ResultsParameter-efficient fine-tuning substantially improved both entity and relation extraction performance across all tasks and model families. Fine-tuned Qwen3-8B achieved the strongest overall performance with 89.42% micro-averaged entity F1 and 62.32% micro-averaged relation F1. Fine-tuned Llama-3.2-3B achieved 87.63% entity F1 and 58.42% relation F1 despite its substantially smaller parameter count, outperforming the zero-shot 8B model on both tasks. Fine-tuning also reduced structured JSON parse failures from 23.5% to 0.11%, demonstrating stable schema internalization during supervised adaptation. ConclusionSchema-grounded multitask instruction tuning with LoRA provides a robust and computationally feasible framework for unified biomedical information extraction across heterogeneous benchmark corpora. The findings further demonstrate that schema-grounded adaptation is substantially more important than model scale alone for reliable extraction of structured biomedical relations. The gap between NER and relation extraction performance motivates future research on explicit negative-relation supervision and ontology-guided relation extraction.