In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains a significant challenge. This limitation poisons large language models to correctly understand relationships between biological entities, such as molecules and diseases, or drug interactions, and further results in potential misinterpretation of biomedical documents. To address this issue, current approaches generally adopt the Synthetic Data Augmentation method which involves similarity computation followed by word replacement, but counterfactual data are usually generated. As a result, these methods disrupt meaningful word sets or produce sentences with meanings that deviate substantially from the original context, rendering them ineffective in improving model performance. To this end, this paper proposes a biomedical-dedicated rationale-based synthetic data augmentation method. Beyond the naive lexicon similarity, specific bio-relation similarity is measured to hold the augmented instance having a strong correlation with bio-relation instead of simply increasing the diversity of augmented data. Moreover, a multi-agents-involved reflection mechanism helps the model iteratively distinguish different usage of similar entities to escape falling into the mis-replace trap. We evaluate our method on the BLURB and BigBIO benchmark, which includes 9 common datasets spanning four major BioNLP tasks. Our experimental results demonstrate consistent performance improvements across all tasks, highlighting the effectiveness of our approach in addressing the challenges associated with data scarcity and enhancing the overall performance of biomedical NLP models.

翻译：在生物医学自然语言处理（BioNLP）任务中，如关系抽取、命名实体识别和文本分类，高质量数据的稀缺性仍然是一个重大挑战。这一限制导致大语言模型难以正确理解生物实体（如分子与疾病）或药物相互作用之间的关系，并进一步造成对生物医学文献的潜在误读。为解决此问题，现有方法通常采用合成数据增强方法，该方法涉及相似度计算及随后的词语替换，但通常会生成反事实数据。因此，这些方法破坏了有意义的词语集合，或产生语义与原上下文显著偏离的句子，从而无法有效提升模型性能。为此，本文提出了一种基于原理的生物医学专用合成数据增强方法。该方法超越了简单的词汇相似度计算，通过测量特定的生物关系相似度，确保增强的实例与生物关系保持强相关性，而非仅仅增加增强数据的多样性。此外，一个涉及多智能体的反思机制帮助模型迭代区分相似实体的不同用法，以避免陷入误替换的陷阱。我们在BLURB和BigBIO基准上评估了我们的方法，该基准涵盖四大BioNLP任务中的9个常用数据集。实验结果表明，该方法在所有任务上均取得了持续的性能提升，突显了其在应对数据稀缺挑战及提升生物医学NLP模型整体性能方面的有效性。