CDS-trained BabyLMs show earlier and more appropriate production in a new frame-completion task while FineWeb-edu models lead on comprehension benchmarks, indicating current tests underestimate CDS benefits.
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LLMs learn causal relations in text via variational induction by detecting difference-makers in word sequences across diverse training data.
AI value alignment is reconceptualized as a pluralistic governance problem arising along three axes—objectives, information, and principals—making it inherently context-dependent and unsolvable by technical design alone.
citing papers explorer
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Child-directed speech facilitates production, not comprehension, in BabyLMs
CDS-trained BabyLMs show earlier and more appropriate production in a new frame-completion task while FineWeb-edu models lead on comprehension benchmarks, indicating current tests underestimate CDS benefits.
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Words as Difference Makers: How Large Language Models Determine Causal Structure in Text
LLMs learn causal relations in text via variational induction by detecting difference-makers in word sequences across diverse training data.