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README.md

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+# 🔥 NEW! BLiSS: Evaluating Bilingual Learner Competence in Second Language Small Language Models
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+Cross-lingual extensions of the BabyLM Shared Task beyond English incentivise the development of Small Language Models that simulate a much wider range of language acquisition scenarios, including code-switching, simultaneous and successive bilingualism and second language acquisition. However, to our knowledge, there is no benchmark of the formal competence of cognitively-inspired models of L2 acquisition, or **L2LMs**. To address this, we introduce the **Benchmark of Learner Interlingual Syntactic Structure (BLiSS)**. 
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+BLiSS consists of 1.5M naturalistic minimal pairs dataset derived from errorful sentence–correction pairs in parallel learner corpora. These are systematic patterns --overlooked by standard benchmarks of the formal competence of Language Models -- which we use to evaluate L2LMs trained in a variety of training regimes on specific properties of L2 learner language to provide a linguistically-motivated framework for controlled measure of the interlanguage competence of L2LMs.
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 # Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies. Available from: https://arxiv.org/abs/2410.22886. 
 
 Salhan et al (2024) creates age-ordered corpora of Child-Directed Speech for four typologically distant language families to implement SSLMs and acquisition-inspired curricula cross-lingually.