arXiv:2606.03085v1 Announce Type: cross Abstract: Causal tracing systematically intervenes on a large language model's (LLM's) internal representations to uncover and quantify the causal pathways linking specific inputs or computations to specific metrics of interest, quantifying the LLM's behavior. Building on previous single-component or single-layer studies, this paper presents a unified framework for causally tracing multiple components simultaneously. This framework systematically identifies the subsets of components (e.g., attention heads and multi-layer perceptron neurons) most critical
The rapid advancement and increasing complexity of large language models necessitate more sophisticated interpretive tools to understand their internal workings and ensure reliability.
This development allows for a deeper and more precise understanding of how LLMs arrive at their outputs, which is crucial for improving their performance, trustworthiness, and safety across various applications.
The ability to causally trace multiple components simultaneously moves from single-component analysis to more holistic insights into LLM behavior, enabling more targeted interventions and debugging.
- · AI researchers
- · LLM developers
- · AI safety organizations
- · Enterprises deploying LLMs
- · Black-box AI systems
- · Debugging via trial-and-error
Improved interpretability of LLMs will lead to more robust and reliable AI systems.
Enhanced debugging capabilities will accelerate LLM development cycles and aid in fine-tuning for specialized tasks.
A deeper understanding of emergent LLM behaviors could inform new architectural designs and mitigate potential biases or undesirable outputs at a fundamental level.
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Read at arXiv cs.CL