@inproceedings{patel-etal-2026-xlm,
title = "x{LM}: A Python Package for Non-Autoregressive Language Models",
author = "Patel, Dhruvesh and
Maram, Durga Prasad and
Chintha, Sai Sreenivas and
Rozonoyer, Benjamin and
McCallum, Andrew",
editor = "Croce, Danilo and
Leidner, Jochen and
Moosavi, Nafise Sadat",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-demo.31/",
pages = "445--456",
ISBN = "979-8-89176-382-1",
abstract = "In recent years, there has been a resurgence of interest in non-autoregressive text generation in the context of general language modeling. Unlike the well-established autoregressive language modeling paradigm, which has a plethora of standard training and inference libraries, implementations of non-autoregressive language modeling have largely been bespoke making it difficult to perform systematic comparisons of different methods. Moreover, each non-autoregressive language model typically requires it own data collation, loss, and prediction logic, making it challenging to reuse common components. In this work, we present the XLM python package, which is designed to make implementing small non-autoregressive language models faster with a secondary goal of providing a suite of small pre-trained models (through a companion package) that can be used by the research community."
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%0 Conference Proceedings
%T xLM: A Python Package for Non-Autoregressive Language Models
%A Patel, Dhruvesh
%A Maram, Durga Prasad
%A Chintha, Sai Sreenivas
%A Rozonoyer, Benjamin
%A McCallum, Andrew
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F patel-etal-2026-xlm
%X In recent years, there has been a resurgence of interest in non-autoregressive text generation in the context of general language modeling. Unlike the well-established autoregressive language modeling paradigm, which has a plethora of standard training and inference libraries, implementations of non-autoregressive language modeling have largely been bespoke making it difficult to perform systematic comparisons of different methods. Moreover, each non-autoregressive language model typically requires it own data collation, loss, and prediction logic, making it challenging to reuse common components. In this work, we present the XLM python package, which is designed to make implementing small non-autoregressive language models faster with a secondary goal of providing a suite of small pre-trained models (through a companion package) that can be used by the research community.
%U https://aclanthology.org/2026.eacl-demo.31/
%P 445-456
Markdown (Informal)
[xLM: A Python Package for Non-Autoregressive Language Models](https://aclanthology.org/2026.eacl-demo.31/) (Patel et al., EACL 2026)
ACL
- Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer, and Andrew McCallum. 2026. xLM: A Python Package for Non-Autoregressive Language Models. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 445–456, Rabat, Marocco. Association for Computational Linguistics.