The use of diverse pre-training datasets can impact GoLLIE's performance on ambiguous or coarse labels by exposing it to a wider range of linguistic contexts and label variations. By training on diverse datasets with varying levels of granularity and ambiguity, GoLLIE can learn robust representations that generalize well across different types of labels. This exposure helps the model better understand subtle distinctions between similar labels and improves its ability to handle ambiguous entities effectively during inference. Additionally, training on diverse datasets enhances GoLLIE's adaptability when faced with novel or unseen label categories by providing a broader knowledge base for making informed predictions.