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

Published: October 22, 2025 | arXiv ID: 2510.19419v1

By: Yuan Gao , Suchir Salhan , Andrew Caines and more

Potential Business Impact:

Tests if AI learns language like kids do.

Business Areas:
Natural Language Processing Artificial Intelligence, Data and Analytics, Software

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.

Country of Origin
🇬🇧 United Kingdom

Repos / Data Links

Page Count
15 pages

Category
Computer Science:
Computation and Language