mlsanityData to 14 Oct 2026

GLADIS: A General and Large Acronym Disambiguation Benchmark

arXiv 2302.01860 ↗First posted 3 Feb 2023cs.CLcited by 5

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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.

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

27 of 74 references.

References · 27

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15 more
  1. 2019Fine-Grained Entity Typing for Domain Independent Entity Linkingcited by 36
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024Logical Consistency of Large Language Models in Fact-checkingcited by 15
  2. 2026LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptationcited by 6
  3. 2025EMODIS: A Benchmark for Context-Dependent Emoji Disambiguation in Large Language Modelscited by 0
  4. 2025DACE For Railway Acronym Disambiguationcited by 0
  5. 2026PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguationcited by 0