# Context Is King: Large Language Models' Interpretability in Divergent Knowledge Scenarios
## 2 Related Work
### Explainable AI and Interpretability in Large Language Models  
The transformative impact of LLMs opens up new possibilities for XAI research, posing a major challenge in terms of their explainability, interpretability, and ethical and reliable use. The traditional methods and strategies for interpreting machine learning models, such as feature importance analysis and decision tree visualization, are not directly applicable to LLMs due to their massive size and complex architecture. Therefore, researchers have explored various approaches tailored to the unique characteristics of LLMs to provide insights into their decision-making processes [43]. One prominent approach to enhancing the interpretability of LLMs involves the use of attention mechanisms [44]. Attention mechanisms, which are integral to the architecture of many LLMs, have been leveraged to provide insights into which parts of the input data the model is focusing on when making predictions. For instance, studies have shown that visualizing attention weights can help to trace the reasoning paths taken by the model, thereby offering a glimpse into its decision-making process [45]. However, the complexity of attention patterns often requires sophisticated methods to decode meaningful explanations. Another significant area of research has been the development of post hoc explanation techniques [26]. These methods aim to provide insights into the decision-making processes of LLMs without altering their underlying structure. One common approach is the use of Local Interpretable Model-agnostic Explanations (LIMEs) [46], which approximate the model locally with a simpler interpretable model to explain individual predictions. Another technique, Shapley Additive Explanations (SHAPs) [47; 48], leverages cooperative game theory to attribute the contribution of each feature to the final prediction, offering a detailed understanding of feature importance. Additionally, counterfactual explanations have gained appeal [49], where slight modifications are made to the input data to observe how these changes affect the output, thereby highlighting the critical factors influencing the model's decisions. These post hoc methods are essential for uncovering biases, identifying potential errors, and enhancing user trust in LLMs, especially in high-stakes applications. Despite these advancements, challenges remain to ensure that the explanations provided by XAI techniques are faithful, understandable, and meaningful. The inherent complexity of LLMs means that, even with advanced methods like attention visualization and post hoc techniques, it is difficult to guarantee that the explanations accurately reflect the true reasoning process of the model. These techniques can sometimes oversimplify the complex operations within LLMs, posing the risk of misrepresentation. Additionally, research challenges persist, such as providing explanations without ground-truths, developing metrics to measure the quality of explanations, mitigating biases, addressing hallucinations, and adapting traditional interpretability techniques to the scale and complexity of LLMs. There is also ongoing exploration into understanding model decisions through contextual explanations and explainable prompting [50,51]. A key differentiator between LLMs and traditional artificial intelligence models is their ability to accept input data in the form of natural language during model inference [52]. This characteristic offers more intuitive and user-friendly insights into these powerful models' decision-making processes, enabling us to analyze, understand, and draw conclusions using natural language. Indeed, such techniques are being widely adopted and have proven to be effective in addressing the problems of knowledge cutoffs and accessing up-to-date information [53,54]. Building on these foundations, our work proposes a novel approach to interpretability through context-based interaction techniques.