# QQ: A Language Metadata Toolkit for Multilingual NLP

arXiv: https://arxiv.org/abs/2603.00620 · HTML: https://mlsanity.com/paper/2603.00620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language](https://mlsanity.com/paper/2506.20920/) (2025)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [GlotScript: A Resource and Tool for Low Resource Writing System Identification](https://mlsanity.com/paper/2309.13320/) (2023)
- [Confounding Factors in Relating Model Performance to Morphology](https://mlsanity.com/paper/2511.01380/) (2025)
- [Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Right](https://mlsanity.com/paper/2507.03473/) (2025)
- [Typologically Informed Parameter Aggregation](https://mlsanity.com/paper/2601.16629/) (2026)

## Built on by (most cited first, 1 of 1)

- [Seeing the Unseen: Visual Similarity for Pixel Language Model Adaptation](https://mlsanity.com/paper/2608.30541/) (2026)

## Abstract (arXiv)

Multilingual NLP research increasingly involves hundreds or thousands of languages across different datasets. Managing, discovering, and reporting language metadata becomes a common hurdle at these scales. We present QQ, a metadata toolkit and browser explorer. QQ compiles language metadata sources into a graph of language varieties, scripts, regions, identifiers, names, and relations, and exposes it through a Python API, a command-line interface, and a browser-based explorer. Users can normalize identifiers, retrieve metadata, traverse relations, and discover which external resources contain a language. We demonstrate QQ on three workflows: an audit of the HuggingFace Hub, linking resources that use different identifier systems, and generating reproducible language-reporting tables. QQ supports FAIR-oriented metadata practices through versioning, open formats, and reusable interfaces.