XLM: A Python package for non-autoregressive language models

TL;DR

XLM is a Python package that simplifies the development and evaluation of non-autoregressive language models, offering reusable components and pre-trained models to facilitate research in this area.

Contribution

The paper introduces XLM, a Python package that standardizes and accelerates the implementation of non-autoregressive language models with pre-trained model support.

Findings

Provides a unified framework for non-autoregressive models

Includes pre-trained models for immediate use

Facilitates systematic comparison of methods

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 xlm-models package) that can be used by the research community. The code is available at https://github.com/dhruvdcoder/xlm-core.