## 5 Experimental Framework and Results  
This section describes the key elements employed in our experimental framework and outlines a case study comparison of explanation approaches (Section 5.1) alongside a comparative analysis of RAG and CARAG methods (Section 5.2). The experimental framework integrates custom Python modules for data management, clustering, embedding generation, and fact verification, leveraging purpose-built methods and pre-trained models for embedding and explanation generation. For embedding generation, we selected the Sentence-BERT (SBERT) model [53], which enhances BERT by incorporating siamese and triplet network structures to produce semantically meaningful sentence vectors. Specifically, we employed the open-source _all-mpnet-base-v2_ variant of SBERT, fine-tuned on over 1 billion textual pairs. This model is relevant in our methodology, where cosine similarity supports context filtering and nuanced textual similarity [54]. Moreover, we chose to use the same SBERT encoder as employed in the SOI methodology [49] to ensure consistency and comparability. Exploring alternative embedding models to assess their impact on the pipeline's performance remains an avenue for future work. For evidence-retrieval tasks, we integrated the FAISS (Facebook AI Similarity Search) library [55] into our pipeline. FAISS enables rapid similarity searches on large datasets, managing vectorized storage of our corpus to facilitate document retrieval. By indexing the _all-mpnet-base-v2_ embeddings generated from our dataset, FAISS scales evidence retrieval efficiently. This setup allows both RAG and CARAG to retrieve evidence directly from the indexed vectors, thereby supporting explanation generation and subsequent processes. For fact verification and explanation generation, we employed the _Llama-2-7b-clat-lf_ variant of LLaMA from Meta [56], chosen for its balance of efficiency and performance and its compatibility with our computational resources. With 7 billion parameters, Llama-2 Chat is suited to our explainability tasks, offering competitive performance comparable to models like ChatGPT and PaLM [56]. Optimized for dialogue and trained with RLHF, the model supports our informed prompting methodology (Section 4.3) to generate  
Figure 5: Overview of the CARAG Framework vs. Standard RAG. The Standard Retrieval path (red arrows) retrieves evidence solely based on the claim vector, without incorporating contextual information. In contrast, the CARAG pipeline (light blue-shaded area) introduces context-aware evidence retrieval, which includes the generation of SOI, followed by thematic embedding aggregation and its weighted combination with claim embeddings for evidence retrieval. Retrieved documents are then passed to the Augmented Generation Pipeline (yellow-shaded area), where an LLM generates explanations based on the claim, retrieved evidence, and an instructional prompt. coherent, user-aligned explanations. Implemented using the _AutoTokenizer_ and _AutoModelForCausalLIM_ classes from Hugging Face, the process follows a sequence-to-sequence (seq-to-seq) approach, where the input sequence combines the claim text, retrieved evidence, and an instructional prompt. The output sequence includes natural language reasoning, providing a verdict on the claim and a nuanced post-hoc explanation. Operating in zero-shot mode, the model leverages its pre-trained linguistic and contextual capabilities without task-specific fine-tuning. An example of this workflow is detailed in Section 5.1. To ensure unbiased results, GPU memory is cleared before each generation run. Future work could explore newer versions, such as Llama 3 [45], on advanced hardware to assess potential improvements. To visualize the thematic clusters and SOI of a claim, as shown in Figure 6, we used the NetworkX Python package and Plotly's graph objects.