BLiSS 1.0: Evaluating Bilingual Learner Competence in Second Language Small Language Models
Gao, Yuan, Salhan, Suchir, Caines, Andrew, Buttery, Paula, Sun, Weiwei
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Oct-23-2025
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