One potential bias in using Hindi Wikipedia data for generating the Suvach dataset is the inherent bias present in the Wikipedia articles themselves. Wikipedia articles may reflect certain perspectives or biases of the contributors, leading to skewed information or limited coverage of certain topics. This bias can impact the quality and diversity of the dataset generated from these articles.
To mitigate this bias, it is essential to diversify the sources of data used for generating prompts. Incorporating data from multiple sources, such as reputable news websites, academic papers, or government reports, can help reduce the reliance on a single biased source like Wikipedia. Additionally, implementing a robust validation process that checks for accuracy, relevance, and diversity of information in the generated questions can help identify and address any biases present in the dataset.
Furthermore, conducting a thorough analysis of the dataset to identify and mitigate any biases or limitations before using it for training and evaluation can help ensure the quality and fairness of the Suvach benchmark.