# Context Is King: Large Language Models' Interpretability in Divergent Knowledge Scenarios
## 1 Introduction  
Large language models (LLMs) have ushered in a new era of artificial intelligence (AI), fundamentally transforming our interaction with technology and unlocking unprecedented capabilities. The introduction of models such as BERT [1] and the subsequent release of ChatGPT, based on the GPT-3 model [2], acted as a catalyst, sparking widespread excitement and revolutionizing the LLM landscape. Following these releases, a new wave of LLMs flooded the field, with models such as Llama [3, 4], the Gemini family [5], Mistral [6], and Phi [7]. Built on massive datasets and sophisticated architectures, these models have an unparalleled ability to understand, reason, and generate human-like text. Leading tech companies like Google, Meta, and Microsoft have developed their own models, integrating and deploying them into their commercial products to enhance their functionalities. The emergence of the first open-source models has further democratizedtechnology access, enabling the broader community to integrate and utilize these models in a wide range of applications. From facilitating natural language understanding to enabling creative content generation, LLMs have become indispensable tools across academia and industry. They are being used for tasks such as automated customer support [8], personalized recommendations [9], real-time translation [10], summarization of complex documents [11], and even aiding in scientific research by analyzing large datasets, generating hypotheses and better representations [12]. The versatility and robustness of these models make them critical assets in driving innovation and efficiency across diverse sectors. However, as these models have grown more powerful, they have also become increasingly complex, resembling opaque black boxes whose inner workings are difficult to interpret. This lack of transparency poses significant challenges for model interpretability and accountability [13,14]. The intricate architectures and vast parameter spaces make it challenging to understand how these models arrive at their decisions. This opacity can lead to several negative consequences, such as the generation of fictitious information (typically called hallucinations) [15], where the model produces plausible-sounding but incorrect or nonsensical outputs. In addition, without a clear understanding of the decision-making process, it becomes difficult to identify and mitigate biases embedded in the models [16], potentially leading to unfair or discriminatory outcomes [17, 18, 19]. The lack of transparency not only undermines user trust but also raises ethical and security concerns, particularly when LLMs are used in critical applications such as healthcare [20], legal advice [21], education [22], and autonomous systems [23]. Addressing these issues is crucial to ensuring the responsible and effective use of LLMs and highlights the need for robust explainability frameworks and methods that illustrate the decision-making processes of these models. In terms of artificial intelligence, explainability refers to the ability to understand, explain, and interpret the decisions and behavior of AI systems in human terms [24, 25, 26]. Within LLMs, explainability is particularly crucial due to the models' inherent complexity and the high stakes of their applications. The ability to provide clear, understandable explanations for the decisions made by LLMs can significantly enhance user trust and facilitate the adoption of these technologies in sensitive areas. Given the opaque nature of LLMs, achieving explainability requires innovative approaches that go beyond traditional model introspection. One promising direction is the use of contextual information to illuminate the decision-making processes of these models, enhancing interpretability. Such techniques have proven to be effective in dealing with knowledge cutoffs and the need to access updated content without retraining the model, as well as mitigating issues such as biases and hallucinations [27].