Fig. The figure compares the performance of fine-tuned specialized NER models and in-context learning with large language models on a NER task in materials fatigue domain. For each specialized NER model, the mean F1-score achieved on validation and test datasets across five random initializations are presented. The BERT, SciBERT, and MatSciBERT models (distinct model initializations) were fully fine-tuned while only the last layer of the DeBERTa model (distinct architecture) was fine-tuned. Additionally, parameter-efficient fine-tuning was also performed for these models using LoRA. ICL of GPT-4 and GPT-3.5-turbo models were performed.