## 2 Background  
The evolution of AFV systems began with 'knowledge-free' approaches relying solely on linguistic features of claims for verification, without using external evidence [34]. This was followed by the integration of structured knowledge bases, like RDF triples, for fact verification, but faced challenges with scalability and handling nuanced information [35; 36]. A major advancement in AFV systems came with the introduction of evidence-retrieval methods, where claims were verified against retrieved textual sources, such as Wikipedia, as demonstrated in the FEVER dataset by [4]. While Wikipedia offered broad accessibility, it also introduced challenges with comprehensiveness and potential biases, impacting the fidelity of resulting AFV models [37]. A further breakthrough was achieved with the development of advanced retrieval capabilities, exemplified by RAG, which dynamically integrate external knowledge during inference to enable more context-sensitive and informed veracity predictions [38]. Nonetheless, interpretability, accuracy, and fidelity remain essential paradigms in explainable AI, as emphasized by recent work on XAI [1; 13; 31]. Maintaining these XAI principles has become increasingly challenging due to the evolving complexity of modern AFV systems, particularly with the use of pre-trained Foundation Models (FM) and Large Language Models (LLM) in different roles across the AFV pipeline. For instance, LLMs are used as encoders for embedding generation to capture semantic representations of claims and evidence, for natural language inference (NLI) in veracity prediction, and for natural language generation (NLG) in crafting coherent explanations. Additionally, incorporating RAG for dynamic evidence retrieval and in-context learning for veracity assessment based on retrieved evidence [38] has further increased the complexity of these systems. In particular, the use of LLMs, with their massive scale in terms of parameters and training data, presents unique challenges for explainability in downstream tasks like AFV. Moreover, these models require extensive computational resources for generating explanations. Consequently, established interpretability methods, including feature attribution methods such as gradient-based approaches [39] and SHAP values [40], as well as surrogate models like LIME [41], can become computationally impractical for explaining models with billions of parameters, limiting their feasibility for current AFV systems compared to traditional deep learning models. In addition to the influence of the operational role of LLM integration we discussed, the training paradigm adopted for the employed LLM also necessitates diverse strategies for achieving XAI in AFV. Specifically, the approach to explainability varies significantly based on whether the model is fine-tuned or used directly through prompting. In the fine-tuning paradigm, models like BERT [42] and RoBERTa [43], which are pre-trained on large corpora, are subsequently fine-tuned on labeled datasets for specific tasks, such as AFV. In contrast, the prompting paradigm utilizes models without additional training, as seen with base models like GPT-3 [44] and Llama 3 [45], which respond based on pre-trained knowledge; or as with assistant models like GPT-4 by OpenAI [46], Claude by Anthropic [47], which undergo additional alignment through methods like instruction tuning and Reinforcement Learning from Human Feedback (RLHF) to perform user-specific tasks [30]. The prompting paradigm is further reinforced by the impressive zero-shot performance of LLMs in various language tasks [48], showcasing their capability to handle complex tasks without task-specific fine-tuning. These diverse methods of employing LLMs significantly affect how XAI research in modern AFV is approached. Fine-tuned models require tailored interpretability methods that account for task-specific adjustments, whereas prompting-based models rely on post-hoc explanations generated from the models' pre-trained knowledge [30].