# Context Is King: Large Language Models' Interpretability in Divergent Knowledge Scenarios
## 1 Introduction
Furthermore, the widespread adoption of these techniques is driven by the ease of creating LLM-based applications and gaining greater control over data. By situating LLMs within specific controlled contexts, we can better interpret and understand the decision-making processes. In this work, we propose a novel approach to interpretability by leveraging interaction techniques that provide additional context to LLMs. Rather than dissecting the LLMs themselves, we focus on how contextual information can be utilized to interpret and explain the models' reasoning, chains of thoughts, and outcomes. The aim of this study is to evaluate this capability, shedding light on the reliability and interpretability of LLM outputs in relation to the popular interaction techniques used today. This complements, rather than replaces, the existing explainability and interpretability techniques in the literature. To test our approach, we introduce the Context-Driven Divergent Knowledge Evaluation (CDK-E) methodology, designed to evaluate the interpretability of large language models within context-divergent scenarios. The novelty of our contribution lies in how we incorporate fabricated contexts to systematically test a model's ability to adapt its reasoning process. This is achieved through the Divergent Knowledge Dataset (DKD), which includes a series of questions based on fabricated historical events and facts that act as context, diverging from the model's inherent knowledge. The CDK-E evaluates whether the model can produce accurate and contextually aligned responses, providing insights into how its reasoning adapts to the given context. Our methodology establishes a baseline using state-of-the-art LLMs through prompt engineering and a robust performance assessment module. By structuring the experiment around divergent contexts, we directly address the research question of how well LLMs can align their outputs with external information, offering a more thorough assessment of their interpretability in complex scenarios. The empirical results show that advanced LLMs achieve strong alignment with the provided divergent contexts, confirming our hypothesis that contextual information significantly enhances interpretability. Furthermore, the strong correlation between LLM-based metrics and semantic evaluations affirms the reliability of our assessment module. The main contributions of this paper are as follows:  
* The introduction of the Context-Driven Divergent Knowledge Evaluation (CDK-E) methodology along with the Divergent Knowledge Dataset (DKD), a novel methodology and dataset for evaluating the interpretability of LLMs in context-specific scenarios that diverge from the model's inherent knowledge. * The presentation of empirical results demonstrating the effectiveness of contextual information in achieving interpretability and explainability. * The provision of analysis and discussion for the integration of LLMs, focusing on improving interpretability and enhancing the understanding of their decision-making processes, along with a framework for future research in explainable AI (XAI). The rest of the paper is organized as follows: Section 2 discusses the related work and context-based interaction techniques, Section 3 presents the CDK-E methodology and the DKD, Section 4 describes the experimental setup, Section 5 details the results and discussion, and Section 6 provides the conclusions of our work.