@inproceedings{poche-etal-2026-interpreto,
title = "Interpreto: An Explainability Library for Transformers",
author = "Poch{\'e}, Antonin and
Mullor, Thomas and
Sarti, Gabriele and
Boisnard, Fr{\'e}d{\'e}ric and
Friedrich, Corentin and
Claye, Charlotte and
Hoofd, Francois and
Bernas, Raphael and
Asher, Nicholas and
Hudelot, Celine and
Jourdan, Fanny",
editor = "Durrett, Greg and
Jian, Ping",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-demo.1/",
pages = "1--13",
ISBN = "979-8-89176-392-0",
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 feature-level attributions and is uncommon in existing libraries."
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<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 feature-level attributions and is uncommon in existing libraries.</abstract>
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%0 Conference Proceedings
%T Interpreto: An Explainability Library for Transformers
%A Poché, Antonin
%A Mullor, Thomas
%A Sarti, Gabriele
%A Boisnard, Frédéric
%A Friedrich, Corentin
%A Claye, Charlotte
%A Hoofd, Francois
%A Bernas, Raphael
%A Asher, Nicholas
%A Hudelot, Celine
%A Jourdan, Fanny
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F poche-etal-2026-interpreto
%X 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 feature-level attributions and is uncommon in existing libraries.
%U https://aclanthology.org/2026.acl-demo.1/
%P 1-13
Markdown (Informal)
[Interpreto: An Explainability Library for Transformers](https://aclanthology.org/2026.acl-demo.1/) (Poché et al., ACL 2026)
ACL
- Antonin Poché, Thomas Mullor, Gabriele Sarti, Frédéric Boisnard, Corentin Friedrich, Charlotte Claye, Francois Hoofd, Raphael Bernas, Nicholas Asher, Celine Hudelot, and Fanny Jourdan. 2026. Interpreto: An Explainability Library for Transformers. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 1–13, San Diego, California, United States. Association for Computational Linguistics.