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Datasets for Verb Alternations across Languages: BLM Templates and Data Augmentation Strategies

Summary

Large language models handle many single-sentence phenomena well, but it is less clear whether they grasp paradigmatic regularities that only emerge across several related sentences. Verb alternations are one such case. Samo and Merlo address this gap with multilingual datasets in the Blackbird Language Matrices (BLM) format. A BLM is a language analogue of Raven-style or ARC puzzles: a model sees an incomplete sequence of sentences and must choose the right continuation from a set of minimally contrasting candidates.

The datasets cover:

  • change-of-state and object-drop verbs in English, German and Italian
  • binyanim in Hebrew

The authors define three template families of increasing complexity. They populate them through three linguistically informed augmentation routes: masked-model generation, LLM prompting, and sentences drawn from natural corpora. Each template has a low and a high lexical-variation version.

The result is several thousand BLM problems, accompanied by simple baselines. The baselines show the data can expose what models get wrong, but they are not meant as a full evaluation. The resource gives a controlled, cross-lingual way to test systematic knowledge of argument structure.

Key points

  • Template types:
    • Type A uses cues outside the alternation itself, such as a temporal PP like "two years ago".
    • Type B uses linguistic diagnostics, such as subject relative clauses.
    • Type C mixes both and also varies inflection, such as tense.
  • Morphological contrasts across languages:
    • English marks the alternation with nothing overt.
    • Italian uses the reflexive-like si.
    • German uses a nominative/accusative case shift.
    • Hebrew relies on root-and-template morphology.
  • Lexical variation: each matrix comes in a MinLex version, where lexical material is shared across sentences, and a MaxLex version, where sentences share structure but not words.
  • Answer sets: wrong candidates contain either sequence errors (wrong constituent order) or grammar errors (constituents of the wrong syntactic or semantic kind).
  • Error analysis: the appendix gives raw error counts by error type for COS and OD in English and Italian, using test sets of 300 items per condition.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4t48qjruy2ce

@inproceedings{samo2026datasets,
  title = {{Datasets for Verb Alternations across Languages: BLM Templates and Data Augmentation Strategies}},
  author = {Samo, Giuseppe and Merlo, Paola},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4t48qjruy2ce},
  url = {https://doi.org/10.63317/4t48qjruy2ce}
}

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