arXiv:2511.21594v3 Announce Type: replace Abstract: Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to interpret. In this work, we extract, process, and visualize latent state geometries in Transformer-based language models through dimensionality reduction. We capture layerwise activations at multiple points within Transformer blocks and enable systematic analysis through Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). We demonstrate experiments on GPT-2
The increasing scale and complexity of LLMs necessitate advanced tools for internal mechanism interpretation, pushing research into their latent spaces.
Understanding LLM internal mechanisms is crucial for improving their performance, trustworthiness, and mitigating undesirable behaviors, impacting AI development and deployment.
This research provides a more systematic approach to interpreting the 'black box' nature of LLMs, potentially leading to more controllable and predictable AI systems.
- · AI researchers
- · ML engineers
- · AI ethics and safety organizations
- · Proprietary black-box AI models
- · AI developers without interpretability tools
Improved understanding of LLM decision-making and generation processes.
Development of more robust, transparent, and debuggable AI models.
Accelerated progress in AGI development due to deeper insights into complex AI architectures.
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Read at arXiv cs.LG