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.
By Steven Y. Feng, Michael C. Frank
arXiv:2609.22607v1 Announce Type: new
Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas,...
By Minwoo Kang, T\'ea Wright, Seun Eisape, Ayush Raj, Suhong Moon, Joseph Suh, Alane Suhr, David M. Chan, John Canny
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.
By Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam, Mohammad Aliannejadi
The paper introduces a method called randomized intermediate guidance for training tandem speech-to-speech models, where a large language model (LLM) acts as a backend providing candidate responses while the user is speaking. Instead of simulating the backend’s guidance, the approach derives guidance directly from the conversation corpus, using target responses for informative guidance and randomly sampled responses to simulate irrelevant updates. Experiments on synthetic dialogues and 3.8k hours of real conversations show that this technique yields response quality comparable to LLM-generated baselines while improving natural turn‑taking and audio‑judge naturalness.
By Manato Yaguchi, Yotaro Kubo, Hikaru Asano, So Kuroki
arXiv:2609.37121v1 Announce Type: new
Abstract: Cross-linguistic effects are a central topic in bilingual first-language acquisition. Artificial learners can help investigate L1-L2 interactions by en...
By Nikitas Theodoropoulos, Maria Lymperaiou, Giorgos Filandrianos
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.
The paper introduces a KL regularization term that aligns predicted dialogue act distributions with empirical transition patterns derived from a corpus. Applied to a 60‑class German counselling taxonomy, this method improves macro‑F1 scores by 9–42% relative to various encoders and enhances dialogue‑flow alignment. Cross‑dataset validation on the HOPE dataset shows that the gains transfer across languages and counselling domains, and systematic ablations reveal that transition regularization consistently benefits weaker baseline models, especially in fine‑grained, data‑sparse dialogue tasks.
By Eric Rudolph, Philipp Steigerwald, Jens Albrecht
arXiv:2607. 05365v1 Announce Type: cross Abstract: Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech.
By Thomas Thebaud, Yuzhe Wang, Hao Zhang, Sathvik Manikantan Napa Ugandhar, Ashish Hallur, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Velazquez
The paper reviews how large language models (LLMs) are employed to study infant syntax acquisition, focusing on the BabyLM challenge that aims for human‑level syntactic performance using developmentally realistic corpora. It critically examines dataset construction, model selection, training procedures, and syntactic evaluation methods, highlighting methodological assumptions that limit the theoretical reach of these studies. The authors find that using developmentally realistic corpora has only modest impact on benchmark performance, pointing to fundamental computational differences between LLMs and actual infant syntax learners.
By H\'elie Bazin (SCAI, SND, ISIR), Anouk Barberousse (SND), Fran\c{c}ois Yvon (MLIA)
The paper investigates why AI-generated text has become harder to understand, focusing on psycholinguistic features linked to human processing difficulty such as object relative clauses, long syntactic dependencies, and low-frequency n-grams. Using a new corpus of AI-generated text from diverse domains, the study finds that frontier and near-frontier language models employ these difficult features more frequently than paraphrases from round-trip translation and human writing, including more inanimate subjects with animate verbs, longer dependencies, stacked nouns, and reduced object relatives. The authors hypothesize that AI models prioritize information density for tasks like math, code, and reasoning, diverging from human communication goals, and demonstrate this by fine‑tuning a model with a length penalty, which reduces human‑like linguistic behavior.
By Thomas Hikaru Clark, Matias B. Martin, Kyle Mahowald
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv:2610.34215v1 Announce Type: new
Abstract: Synthetic linguistic data have become increasingly prominent in linguistic research, from the analysis of large collections of AI-generated texts to co...
By Giuseppe Samo