Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge. However, most existing approaches rely on supervised models trained on costly annotated datasets, limiting their scalability and adaptability across relation types and domains. We investigate few-shot BioRE using prompt-based learning with large language models (LLMs) and compare two task formulations: pairwise classification, which predicts relations for individual entity pairs, and joint generation, which extracts multiple relations in a single model call. Experiments on the BioREDirect dataset reveal a clear precision-recall trade-off. Pairwise classification achieves higher recall, whereas joint generation is more precise and computationally efficient. The best-performing model achieves a micro-F1 score of 0.44, substantially outperforming previous few-shot results (0.34) while remaining below the supervised baseline (0.56). Much of this gap is attributable to a single ambiguously defined relation type. When evaluated using macro-F1, which better captures performance across relation types in an imbalanced setting, prompt-based approaches outperform the supervised baseline (0.45 vs. 0.38), particularly on rare relation types. These findings highlight the potential of LLMs for BioRE in low-resource settings and underscore the importance of well-defined relation schemas.

翻译：生物医学关系抽取（BioRE）是将生物医学文献转化为结构化知识的关键步骤。然而，现有方法大多依赖基于昂贵标注数据集训练的监督模型，这限制了其在多种关系类型和领域中的可扩展性与适应性。我们研究了基于提示学习的大语言模型（LLM）在少样本BioRE中的应用，并比较了两种任务形式：成对分类（预测单个实体对的关系）和联合生成（通过单次模型调用提取多个关系）。在BioREDirect数据集上的实验揭示了明确的精度-召回权衡：成对分类召回率更高，而联合生成精度更高且计算效率更优。最优模型的微平均F1分数达0.44，显著超越先前少样本方法（0.34），但仍低于监督基线（0.56）。该性能差距主要源于某单一模糊定义的关系类型。当采用更能在非均衡场景下捕捉各类型性能的宏平均F1评估时，基于提示的方法优于监督基线（0.45 vs. 0.38），尤其在稀有关系类型上表现突出。这些发现凸显了大语言模型在低资源场景下进行BioRE的潜力，并强调了定义清晰关系模式的重要性。