Transformers form the backbone of most modern generative models, but their inner workings often raise questions. How exactly does the self-attention mechanism transform a sequence of tokens into contextual representations? Which training stages are responsible for establishing long-term dependencies? I’d love to hear your explanations and recommendations for studying these processes. How do you typically approach dissecting transformer architectures?
How do transformers work in modern generative AI systems?
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When I first tried building a small transformer-based code generation model, I started by implementing the self-attention layer "from scratch." I quickly realized that the key was proper scaling of the dot product between queries and keys (dividing by √d_k). Without this, gradients would vanish quickly, and the model couldn’t establish relationships between tokens. Once I added scaling and multi-head attention, the model started "seeing" context: a query token could influence a token 20 positions away, not just nearby ones.
To capture long-range dependencies, I used a two-stage training approach. First, I trained the model on a small set of synthetic sequences with explicit dependencies (e.g., "A … Z"). This helped the network learn to retain information across multiple layers without excessive noise. Then, I moved to a real code dataset, increasing the number of layers and adding positional embeddings, training for 10–20 epochs. This "trick" sped up convergence and gave more stable long-distance representations.
The best advice I got from colleagues was to regularly visualize attention matrices. Dense heatmaps showed which tokens were actually attending to each other and helped quickly spot "sticking" in local patterns. I also found the original "Attention Is All You Need" paper incredibly useful, paired with Alasdair Wang’s "The Illustrated Transformer" course—both break things down with visual examples, making it much easier to grasp. If you're experimenting, start small, then scale up gradually, checking attention at every step.