arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attribution methods and concept-based explanations. The library bridges recent research and practical tooling by exposing explanation workflows through a unified API for both classification and text generation. A key differentiator is its end-to-end concept-based pipeline (from activation extraction to concept learning, interpretation, and scoring), which goes beyond fea
The rapid advancement and adoption of large language models necessitates better interpretability tools to ensure trust, transparency, and effective deployment across various applications.
Interpreto represents a significant step towards demystifying complex AI models, making them more auditable and reliable for both developers and end-users, especially in critical applications.
The availability of a unified, open-source library specifically designed for transformer model explainability standardizes and simplifies the process of understanding how these powerful models arrive at their decisions.
- · AI developers
- · Auditors and regulators
- · Researchers in AI safety
- · Companies deploying LLMs
- · Proprietary explainability tool vendors (if they cannot differentiate)
- · Companies that rely on 'black box' AI for competitive advantage
Increased adoption of explainable AI practices within organizations utilizing transformer models.
Improved debugging, bias detection, and safety guarantees for AI systems, accelerating their deployment in sensitive sectors.
Enhanced public trust and regulatory acceptance of advanced AI, potentially influencing future AI governance frameworks.
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