Retrieval-augmented generation (RAG) is increasingly popular in information extraction (IE), especially in biological and chemical domains where external knowledge is abundant. Existing RAG-enhanced IE systems, however, typically retrieve for every input, even though retrieval may be unnecessary, introduce distracting evidence, and increase inference cost. To address this issue, we propose RAG Switch, a retrieval decision framework for IE that combines difficulty-aware signals with task-grounded supervision. Specifically, we approximate model-specific training difficulty, quantified by Error L2-Norm (EL2N), from inference-time uncertainty signals, and use it to guide retrieval decisions. We then define retrieval necessity directly from instance-level extraction outcomes without retrieval, yielding task-grounded supervision for learning retrieval decision. Experiments on multiple biological and chemical IE benchmarks across diverse IE backbones show that RAG Switch consistently outperforms representative retrieval decision baselines and achieves a strong efficiency–performance trade-off in end-to-end RAG-enhanced IE pipelines.

Generative methods have recently gained traction in biological and chemical named entity recognition for their ability to overcome tagging limitations and better capture entity-rich contexts. However, under a few-shot environment, they struggle with the scarcity of annotated data and the structural complexity of biological and chemical entities—particularly nested and discontinuous ones—leading to incorrect recognition and error propagation during generation. To address these challenges, we propose SCoNE, a Self-Correcting and Noise-Augmented Method for Complex Biological and Chemical Named Entity Recognition. Specifically, we introduce a Noise Augmentation Module to enhance training diversity and guide the model to better learn complex entity structures. Besides, we design a Confidence-based Self-Correction Module that identifies low-confidence outputs and revises them to improve generation robustness. Benefiting from these designs, our method outperforms the baselines by 1.80 and 2.73 F1-score on the CHEMDNER and microbial ecology dataset Florilege, highlighting its effectiveness in biological and chemical named entity recognition.