While Jensen-Shannon divergence (JSD) is a valuable metric for measuring knowledge conflict due to its symmetric and bounded nature, relying solely on it presents several limitations. One significant limitation is that JSD may not capture the nuances of semantic differences between distributions effectively, particularly in cases where the distributions are similar but contextually misaligned. This could lead to inadequate adjustments in the model's output when subtle conflicts exist.
Moreover, JSD does not account for the qualitative aspects of the information being compared. For instance, two distributions might have a low divergence score, yet one could contain outdated or misleading information that could mislead the model. Therefore, it may be beneficial to incorporate additional metrics that assess the semantic relevance and factual accuracy of the information, such as cosine similarity in embedding space or even more sophisticated measures like the Earth Mover's Distance (EMD), which considers the distribution of probabilities in a more granular manner.
Exploring hybrid approaches that combine JSD with other metrics could provide a more comprehensive understanding of knowledge conflict. For example, integrating a contextualized embedding-based similarity measure could enhance the model's ability to discern when two pieces of information are not just statistically different but also contextually relevant or irrelevant. This multi-faceted approach would allow for a more robust and adaptable conflict resolution mechanism.