CIG scores utterances using novelty, relevance, and implication scope derived from a dynamic semantic memory model, outperforming traditional heuristics in correlating with human judgments on deliberative segments.
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Transformers on impossible-language variants show gradual grammatical sensitivity loss but sharp long-sentence generation failures, supporting generative deficiency as a link to non-attestation.
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CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics
CIG scores utterances using novelty, relevance, and implication scope derived from a dynamic semantic memory model, outperforming traditional heuristics in correlating with human judgments on deliberative segments.
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When transformers learn "impossible" languages, what do they learn?
Transformers on impossible-language variants show gradual grammatical sensitivity loss but sharp long-sentence generation failures, supporting generative deficiency as a link to non-attestation.