An AI engineer is fine-tuning a language model to summarize very long legal documents. The model is underperforming, and the engineer suspects it's failing to capture relationships between pieces of information that are far apart in the text. The model uses positional embeddings based on the frequency parameter formula: $\theta_k = b^{-\frac{2(k-1)}{d}}$ To improve the model's ability to handle these long-range dependencies, the frequencies ($\theta_k$) need to correspond to longer periods. Which parameter in the formula (b or d) should the engineer adjust, and in which direction (increase or decrease), to achieve this? Justify your reasoning.