Entity Replacement

Entity replacement, a technique for modifying text by substituting entities (e.g., names, locations), is increasingly used to evaluate and improve the robustness and factual consistency of natural language processing models, particularly in relation extraction and summarization. Current research focuses on developing methods to generate counterfactual data through entity replacement, enabling the assessment of model reliance on spurious cues versus genuine contextual understanding. This work highlights the fragility of many models to even minor entity changes, motivating the development of more robust training methods and improved evaluation benchmarks. The ultimate goal is to create more reliable and generalizable NLP systems that are less susceptible to biases and better at handling variations in input data.