Towards Long-Horizon Interpretability: Efficient and Faithful Multi-Token Attribution for Reasoning LLMs
arXiv:2602.01914v2 Announce Type: replace Abstract: Token attribution methods provide intuitive explanations for language model outputs by identifying causally important input tokens. However, as modern LLMs increasingly rely on extended reasoning chains, existing schemes face two critical challenges: (1) efficiency bottleneck, where attributing a target span of M tokens within a context of length N requires O(M*N) operations, making long-context attribution prohibitively slow; and (2) faithfulness drop, where intermediate reasoning tokens absorb attribution mass, preventing importance from pr
The rapid development and deployment of increasingly complex LLMs for reasoning tasks necessitate improved interpretability methods to ensure reliability and safety.
Enhanced token attribution will accelerate the development of more trustworthy and explainable AI systems, crucial for widespread adoption in sensitive applications.
The ability to efficiently and faithfully attribute reasoning in long-horizon LLMs could unlock new capabilities for AI debugging, safety, and performance optimization.
- · AI developers
- · AI safety researchers
- · Enterprises deploying LLMs
- · Black-box AI models
- · Legacy interpretability tools
Improved understanding of how complex LLMs arrive at their conclusions, leading to more robust models.
Faster identification and mitigation of biases or erroneous reasoning paths within AI systems.
Potentially a new class of 'self-explaining' AI models reducing the need for post-hoc interpretability.
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Read at arXiv cs.LG