Exposure is Optional: Learning Unlike Coordination in Language Models
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that only same-category constituents can be conjoined, which has been challenged by the many grammatical unlike coordinations found in natural language. Treating language models as a computational testbed, we investigate whether the acquisition of unlike coordination requires direct exposure in the training data, or whether it can emerge organically from general compositional abilities. Using Filtered-Corpus Training (FiCT),
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- PossiblePossibly related (embedding) · 46%Understanding large language models demands distinguishing human projection from machine cognition - Nature →
- PossiblePossibly related (embedding) · 45%What exactly does word2vec learn? →
- PossiblePossibly related (embedding) · 45%Transformer →
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Exposure is Optional: Learning Unlike Coordination in Langua” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Jiamu Luo →
“Exposure is Optional: Learning Unlike Coordination in Language Models”
- LinkedLinked via arxiv author · 85%Shane Steinert-Threlkeld →
“Exposure is Optional: Learning Unlike Coordination in Language Models”