# Context Is King: Large Language Models' Interpretability in Divergent Knowledge Scenarios
## 5 Results and Discussion
### Discussion  
The empirical results obtained confirm that our CDK-E methodology effectively achieves interpretability and explainability for LLMs in context-divergent interactions. This supports the extension of these conclusions to many of the widely used context-based interaction techniques prevalent today. The study contributes valuable insights into whether it is feasible to trust, interpret, and understand model responses when using common context-based interaction techniques.  
Unlike the existing XAI methods, which often focus on examining model internals or analyzing feature importance, CDK-E focuses on the interpretability of outputs in scenarios where the context provided diverges from the model's inherent knowledge. This approach  
Figure 5: Comparison of LAV precision and Acc with Mixtral 8x7B for the 50 contexts of DKD.  
does not aim to replace the current interpretability techniques but rather to complement them by evaluating the models' ability to adapt to and align with new context-specific information. Given the unique scope of our methodology, it was not feasible to directly compare it with traditional interpretability approaches as CDK-E's primary goal is not to probe model internal mechanisms but to assess contextual adaptability and coherence.  
Models with superior performance, such as GPT-4o, align better with divergent knowledge, committing fewer errors overall. This high alignment underscores the potential of advanced LLMs to provide reliable and accurate explanations for their outputs. The correlations observed between LLM-based metrics and cosine similarity metrics, along with tests using real contexts and meticulous manual validations, confirm the reliability of the metrics used for evaluating the methodology.  
While CDK-E demonstrates significant strengths, it is worth noting certain challenges and considerations inherent to this approach. The methodology relies heavily on the construction and quality of divergent contexts, which requires a detailed and resource-intensive annotation process. Additionally, given the focus on context divergence, this study emphasizes controlled static contexts and may require further adaptation to evaluate real-time dynamic scenarios. Nonetheless, these challenges do not detract from the primary contribution of CDK-E as a practical and complementary tool for interpretability.  
The results underscore the significance of context in enhancing the interpretability of LLMs. The ability of these models to align their responses with divergent contexts illustrates that contextual information can be effectively used to mitigate issues like hallucinations and biases. However, CDK-E alone does not fully address all the challenges related to explainability, particularly in cases where contextual misalignment leads to subtle biases that may go unnoticed. Additional layers of analysis, such as user studies or error analyses focused on specific biases, could provide a more holistic understanding of how models interact.  
In conclusion, the CDK-E methodology offers a robust framework for evaluating the interpretability of LLMs in context-divergent scenarios. The high performance of models across multiple metrics reaffirms the effectiveness of leveraging contextual information to enhance model transparency and trustworthiness. Nonetheless, CDK-E should be viewed as one component of a broader XAI toolkit rather than a standalone solution.