arXiv Computation and Language
Synthetic Child-Adult Interactions Are Not Developmentally Plausible
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The study trains small GPT‑2 models on child‑scale language data from dense home recordings (BabyView) and synthetic child‑directed dialogue (TinyDialogues), varying data amount, source, and diversity. Models trained on a single child’s environment predict that child’s language far better than others, a pattern largely driven by lexical and distributional factors as shown by a 5‑gram baseline. While training on multiple children improves predictions on pooled held‑out language, it does not yield a clear advantage on grammatical benchmarks, and naturalistic versus synthetic inputs produce distinct grammatical and lexical‑semantic patterns.
arXiv Computation and Language
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The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
arXiv:2606. 20400v1 Announce Type: new Abstract: Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings.
arXiv Computation and Language
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Learning Natural Conversational Behavior in Tandem Speech-to-Speech Models with Randomized Guidance
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arXiv Computation and Language
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Cross-Linguistic Effects in Bilingual Phoneme BabyLMs
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Hugging Face Trending Papers
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SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks do not capture whether these systems behave naturally in conversations, where timing, turn-taking, prosody, interpersonal stance, language and dialect consistency, and relationship-aware appropriateness jointly shape perceived quality.