Dual Path Attribution: Efficient Attribution for SwiGLU-Transformers through Layer-Wise Target Propagation
arXiv:2603.19742v2 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of transformer-based large language models (LLMs) is crucial for their reliable deployment and effective operation. While recent efforts have yielded a plethora of attribution methods attempting to balance faithfulness and computational efficiency, dense component attribution remains prohibitively expensive. In this work, we introduce Dual Path Attribution (DPA), a novel framework that faithfully traces information flow on the frozen transformer in one forward and one backward pass without requiring
The rapid deployment and increasing complexity of LLMs necessitate more efficient and faithful methods for understanding their internal workings, driving research into advanced attribution techniques.
Improved interpretability of LLMs is critical for debugging, security, safety, and regulatory compliance, accelerating their reliable integration into sensitive applications.
The introduction of DPA offers a more computationally efficient way to attribute internal mechanisms of SwiGLU-Transformers, potentially lowering the barrier to entry for explainable AI research and application.
- · AI researchers
- · LLM developers
- · companies deploying LLMs
- · AI safety auditors
- · Less efficient attribution methods
- · organizations with opaque AI systems
More widespread and accessible analysis of highly complex transformer models.
Faster identification of biases, vulnerabilities, and emergent behaviors in large language models.
Accelerated development of more robust, trustworthy, and auditable AI systems, potentially influencing future AI regulation and adoption.
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