The expert-annotated data used to train the scoring model may exhibit several limitations and biases that could affect the assessment of poetic quality. One potential limitation is the subjectivity inherent in literary evaluation; different experts may have varying interpretations of what constitutes high-quality poetry, leading to inconsistencies in annotations. This subjectivity can result in a scoring model that reflects the biases of the annotators rather than an objective standard of quality.
To address these limitations, it is essential to diversify the pool of experts involved in the annotation process. By including a broader range of perspectives—such as those from different cultural backgrounds, literary traditions, and levels of expertise—the model can be trained on a more comprehensive dataset that captures a wider array of aesthetic values.
Additionally, implementing a multi-faceted evaluation approach that combines expert assessments with quantitative metrics can help mitigate bias. For instance, integrating machine-generated metrics alongside human evaluations can provide a more balanced view of poetic quality. Regularly updating the scoring model with new data and feedback from a diverse set of users can also enhance its adaptability and accuracy over time, ensuring a more comprehensive and unbiased assessment of poetic quality.