# GLADIS: A General and Large Acronym Disambiguation Benchmark

arXiv: https://arxiv.org/abs/2302.01860 · HTML: https://mlsanity.com/paper/2302.01860/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 74 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://mlsanity.com/paper/1904.01557/) (2019)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [Named Entity Disambiguation for Noisy Text](https://mlsanity.com/paper/1706.09147/) (2017)
- [MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining](https://mlsanity.com/paper/2012.13978/) (2020)
- [Deep Contextualized Biomedical Abbreviation Expansion](https://mlsanity.com/paper/1906.03360/) (2019)
- [Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing](https://mlsanity.com/paper/2108.10949/) (2021)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [BERT-based Acronym Disambiguation with Multiple Training Strategies](https://mlsanity.com/paper/2103.00488/) (2021)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)
- [Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings](https://mlsanity.com/paper/1904.00929/) (2019)
- [Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation](https://mlsanity.com/paper/2107.00316/) (2021)
- [MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction](https://mlsanity.com/paper/2202.09694/) (2022)
- [SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation](https://mlsanity.com/paper/2111.14306/) (2021)
- [Affective and cognitive prefrontal cortex projections to the lateral habenula in humans](https://mlsanity.com/paper/1402.2196/) (2014)
- [Self-organized critical balanced networks: a unified framework](https://mlsanity.com/paper/1906.05624/) (2019)

## Built on by (most cited first, 5 of 5)

- [Logical Consistency of Large Language Models in Fact-checking](https://mlsanity.com/paper/2412.16100/) (2024)
- [LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation](https://mlsanity.com/paper/2601.05192/) (2026)
- [EMODIS: A Benchmark for Context-Dependent Emoji Disambiguation in Large Language Models](https://mlsanity.com/paper/2511.07193/) (2025)
- [DACE For Railway Acronym Disambiguation](https://mlsanity.com/paper/2512.18357/) (2025)
- [PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation](https://mlsanity.com/paper/2603.23678/) (2026)

## Abstract (arXiv)

Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguation benchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark named GLADIS with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences; (3) three datasets that cover the general, scientific, and biomedical domains. We then pre-train a language model, AcroBERT, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.