arXiv:2601.16407v3 Announce Type: replace-cross Abstract: Large language models (LLMs) make next-token predictions based on clues present in their context, such as semantic descriptions and in-context examples. Yet, elucidating which prior tokens most strongly influence a given prediction remains challenging due to the proliferation of layers and attention heads in modern architectures. We propose Jacobian Scopes, a suite of gradient-based, token-level causal attribution methods for interpreting LLM predictions. Grounded in perturbation theory and information geometry, Jacobian Scopes quantify
The proliferation of complex LLM architectures necessitates new methods for interpretability to understand their internal decision-making, which is critical for deployment and trust.
Understanding the causal attributions in LLMs can enhance model debugging, safety, and alignment, enabling more reliable and controllable AI systems.
This research introduces gradient-based tools to precisely identify which input tokens drive specific LLM predictions, shifting interpretability from qualitative to quantitative.
- · AI researchers
- · LLM developers
- · AI safety organizations
- · Opaque LLM systems
- · Black-box AI development
Improved debugging and understanding of LLM failures and biases.
Faster development and deployment of more robust and trustworthy AI applications.
Enhanced regulatory frameworks and public trust in AI due to greater transparency.
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Read at arXiv cs.AI