Prof. K.Krampis Blog

Decoding the Black Box: My Journey into LLM Interpretability & the Future of AI Explainability

Welcome to my blog, dedicated to unraveling the fascinating inner workingsof Large Language Models (LLMs) and Artifial Intelligence! As a researcher deeply fascinated by the inner workings of AI, I believe understanding how these models arrive at their answers is just as important – if not more so – than the answers themselves. This blog will focus on “mechanistic interpretability” – the practice of dissecting LLMs to identify the specific patterns of activation, or “features,” within their neural networks that correspond to different concepts and abilities. My goal isn’t just to describe what these models do, but to build a genuine understanding of their internal logic.

I’m particularly interested in how these features emerge during training and how they combine for such human-like intelligence to emerge. This isn’t merely an academic exercise; it’s a crucial step towards building truly trustworthy AI. If we can pinpoint the features responsible for specific behaviors, we can validate them, correct errors, and ultimately, ensure these systems align with human values. Understanding those “emergent abilities” of AI that weren’t explicitly programmed – is key. These abilities aren’t magic; they’re the result of complex interactions within the network, and deciphering those interactions opens up incredible research potential. For example, identifying the features responsible for reasoning, common sense, or even creativity could revolutionize how we design AI systems. Furthermore, a deeper understanding of these internal representations has implications extending far beyond LLMs. The principles we uncover while dissecting these models could inform the development of more interpretable and robust AI across all scientific domains.

My approach won’t be about simplistic explanations or surface-level observations. I’m committed to rigor and depth, exploring the challenges and limitations of current interpretability methods. Simply identifying which parts of the network are active (feature attribution) isn’t enough; we need to know what those activations represent. To that end, I’ll be investigating in my writing techniques for tracking feature evolution, disentangling different factors of variation, and developing “mechanistic probes” – carefully designed experiments to test specific hypotheses about the model’s internal workings. The ultimate aim is to move beyond simply observing behavior to predicting it, enabling us to anticipate potential failures and improve system reliability. I’m also incredibly excited about the potential of “circuit discovery” – identifying identifiable modules within an LLM’s neural network responsible for specific computations. This modular approach could unlock a new level of understanding, allowing us to analyze individual components in isolation and build a comprehensive map of the LLM’s internal landscape.

This blog is my platform for sharing these explorations, oin me as we decode the black box and unlock the potential of Large Language Models, and in doing so making these complex systems more transparent, reliable, and ultimately, more beneficial to society while advancing the field of AI explainability.