The current dataset and annotation process may have inherent biases and limitations that could impact the robustness and representativeness of the NLI4CT task. One potential bias could stem from the selection of clinical trial reports, which may not fully capture the diversity of scenarios and outcomes present in real-world clinical settings. Additionally, the annotation process itself may introduce biases based on the annotators' interpretations and subjective judgments. To address these issues, it is crucial to diversify the dataset by including a broader range of clinical trial reports that cover various medical conditions, treatments, and outcomes. Implementing rigorous annotation guidelines and ensuring inter-annotator agreement can help mitigate biases introduced during the annotation process. Furthermore, conducting bias analyses and sensitivity tests on the dataset can reveal any underlying biases and guide efforts to mitigate them, ultimately enhancing the robustness and representativeness of the NLI4CT task.