Unveiling the Role of Feed-Forward Blocks in Contextualization: An Analysis Using Attention Maps of Large Language Models

Transformer-based models have significantly impacted the field of natural language processing, enabling high-performance applications in machine translation, summarization, and language modeling. Introducing a novel analysis of feed-forward blocks within the Mistral Large model, this research provides critical insights into their role in enhancing contextual embeddings and refining attention mechanisms. By conducting a comprehensive evaluation through quantitative metrics such as perplexity, BLEU, and ROUGE scores, the study demonstrates the effectiveness of fine-tuning in improving model performance across diverse linguistic tasks. Detailed attention map analysis revealed the intricate dynamics between self-attention mechanisms and feed-forward blocks, highlighting the latter's importance in contextual refinement. The findings demonstrate the potential of optimized transformer architectures in advancing the capabilities of LLMs, emphasizing the necessity of domain-specific fine-tuning and architectural enhancements. Empirical evidence presented in this study offers a deeper understanding of the functional contributions of feed-forward blocks, informing the design and development of future LLMs to achieve superior performance and applicability.