mlsanityData to 14 Oct 2026

BLiSS 1.0: Evaluating Bilingual Learner Competence in Second Language Small Language Models

arXiv 2510.19419 ↗First posted 22 Oct 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

To bridge the gap between performance-oriented benchmarks and the evaluation of cognitively inspired models, we introduce BLiSS 1.0, a Benchmark of Learner Interlingual Syntactic Structure. Our benchmark operationalizes a new paradigm of selective tolerance, testing whether a model finds a naturalistic learner error more plausible than a matched, artificial error within the same sentence. Constructed from over 2.8 million naturalistic learner sentences, BLiSS provides 136,867 controlled triplets (corrected, learner, artificial) for this purpose. Experiments on a diverse suite of models demonstrate that selective tolerance is a distinct capability from standard grammaticality, with performance clustering strongly by training paradigm. This validates BLiSS as a robust tool for measuring how different training objectives impact a model's alignment with the systematic patterns of human language acquisition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 22 Oct 2025.

Builds on

14 of 33 references.

References · 14

  1. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 564
  4. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 548
  5. 2017JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correctioncited by 97
  6. 2024MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Seriescited by 67
  7. 2024CroissantLLM: A Truly Bilingual French-English Language Modelcited by 53
  8. 2023Language acquisition: do children and language models follow similar learning stages?cited by 41
  9. 2024Release of Pre-Trained Models for the Japanese Languagecited by 22
  10. 2024Investigating Critical Period Effects in Language Acquisition through Neural Language Modelscited by 16
  11. 2023SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERTcited by 15
  12. 2023Second Language Acquisition of Neural Language Modelscited by 8
2 more
  1. 2025Minimal Pair-Based Evaluation of Code-Switchingcited by 8
  2. 2025On the Acquisition of Shared Grammatical Representations in Bilingual Language Modelscited by 7

Built on by

Not cited yet.