Textual data processing plays a critical role in natural language processing (NLP), particularly with regards to Language and Literature Models’ (LLM) functionality as generic interfaces. These interfaces interpret examples and system instructions articulated in natural language, which can encompass a range of prompts like task instructions and system prompts. Furthermore, an array of methodologies can be utilized to assess text generation models, making the analysis of textual data for LLMs increasingly complex.
IBM Research has responded to this by introducing Unitxt, a platform designed for the processing of unified textual data. The platform’s Python module allows users to handle textual data across various languages via the use of recipes, which are essentially configurable pipelines. These involve operators to load and preprocess data, prepare different prompt portions, or evaluate model predictions. Moreover, Unitxt offers a catalog full of pre-defined recipes for different tasks to encourage reusability.
Unitxt’s modularity allows users to create new recipes by mixing and matching different elements, with more than 100,000 recipe configurations possible. Users can experiment with myriad recipes, tasks, datasets, and formatting options. Furthermore, Unitxt is designed to integrate with existing code, facilitating a smooth transition between libraries. For example, Unitxt can load HuggingFace datasets and give output in the same format, allowing it to seamlessly blend with other software sections.
As LLM capabilities expand, there is a necessity for evaluation frameworks to assess models across numerous datasets, tasks, and settings. Unitxt can serve as a cornerstone for such efforts by simplifying adjustments across several crucial dimensions like languages, tasks, prompt structure, augmentation robustness, and more. Moreover, the Unitxt Catalog allows various projects to share their complete evaluation pipelines, streamlining data preparation and the replication of assessment metrics.
Unitxt addresses some formidable technical challenges associated with merging textual representations from varied data sources for LLM training frameworks. Without a common foundation, integrating data augmentations, multitask learning, and few-shot tuning can be very complicated. However, Unitxt simplifies this process to facilitate the development of secure, efficient, and robust LLMs.
Multiple teams at IBM focusing on different NLP tasks have already started to employ Unitxt as a core utility for LLMs. The model has been used to train and evaluate large language models at IBM. The expectation is that as Unitxt continues to evolve with the help of the open-source community, more people will begin to adopt it. Unitxt’s developers believe that it can enhance LLMs’ capabilities and trustworthiness by unifying textual data processing.
The latest research on Unitxt can be accessed via the project’s paper and GitHub. The research team encourages everyone to follow them on social media platforms and join their communities. Additionally, they offer a newsletter, where the latest updates about Unitxt and other projects can be obtained. To stay in the loop, users are advised to join their Telegram channel.
