arXiv:2606.05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent. Recent work uses Large Language Models (LLMs) to recover causal graphs of external-world processes. Instead, in this paper, we use causal graphs to model LLM inference itself, providing stakeholders with a transparent view of how the model perceives and organizes high-level concepts to produce a prediction. We propose a four-phase method for constructing such graphs. Given a target LLM and a set of textual examples, our method discovers class-discriminative, human-inte
The rapid advancement and deployment of Large Language Models necessitate robust explainability frameworks to foster trust and enable critical oversight, especially as these models are integrated into sensitive applications.
Transparent LLM inference through causal graphs provides a critical mechanism for understanding biases, verifying decision-making processes, and ensuring accountability, which is vital for regulatory acceptance and broad adoption.
This research introduces a novel, structured method for dissecting and visualizing the internal reasoning of LLMs, moving beyond mere input-output explanations to a deeper mechanistic understanding.
- · AI developers
- · Regulatory bodies
- · Ethical AI researchers
- · Industries deploying LLMs
- · Opaque AI systems
- · Proprietary black-box AI models
Increased trust and adoption of LLMs in regulated and high-stakes environments due to enhanced explainability.
Development of new AI auditing tools and standards based on causal graph analysis, leading to more rigorous model evaluations.
A potential shift in AI development methodologies towards 'explainability-by-design,' integrating causal modeling from the outset.
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Read at arXiv cs.LG