XLM: A Python package for non-autoregressive language models
This provides a practical tool for researchers working on non-autoregressive text generation, though it is incremental as it builds on existing methods without introducing new paradigms.
The authors tackled the lack of standardized tools for non-autoregressive language models by developing the XLM Python package, which simplifies implementation and includes pre-trained models for research use.
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.