LLM Explainability with Counterfactual Chains and Causal Graphs

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

Source: arXiv cs.LG — read the full report at the original publisher.

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