Cover Image

Baby Scale: Investigating Models Trained on Individual Children's Language Input

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feng, Steven Y., Tan, Alvin W. M., Frank, Michael C.
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models
by: Zeng, Linda, et al.
Published: (2026)

Is Child-Directed Speech Effective Training Data for Language Models?
by: Feng, Steven Y., et al.
Published: (2024)

Training Language Models with Language Feedback at Scale
by: Scheurer, Jérémy, et al.
Published: (2023)

Mini Minds: Exploring Bebeshka and Zlata Baby Models
by: Proskurina, Irina, et al.
Published: (2023)

Mapping Post-Training Forgetting in Language Models at Scale
by: Harmon, Jackson, et al.
Published: (2025)

How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
by: Frank, Gregory N.
Published: (2026)

Leviathan: Decoupling Input and Output Representations in Language Models
by: Batley, Reza T., et al.
Published: (2026)

To Memorize or to Retrieve: Scaling Laws for RAG-Considerate Pretraining
by: Singh, Karan, et al.
Published: (2026)

Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
by: Krajewski, Jakub, et al.
Published: (2025)

EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
by: Lin, Dongyan, et al.
Published: (2026)

Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
by: Li, Hanchen, et al.
Published: (2026)

Single-pass Detection of Jailbreaking Input in Large Language Models
by: Candogan, Leyla Naz, et al.
Published: (2025)

Model-diff: A Tool for Comparative Study of Language Models in the Input Space
by: Liu, Weitang, et al.
Published: (2024)

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
by: Zhou, Chenxi, et al.
Published: (2025)

SiLQ: Simple Large Language Model Quantization-Aware Training
by: Esser, Steven K., et al.
Published: (2025)

Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Models
by: Burger, Christopher, et al.
Published: (2024)

Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs
by: Cherepanova, Valeriia, et al.
Published: (2024)

Large Language Models are Skeptics: False Negative Problem of Input-conflicting Hallucination
by: Song, Jongyoon, et al.
Published: (2024)

Training Optimal Large Diffusion Language Models
by: Ni, Jinjie, et al.
Published: (2025)

Can Language Models Discover Scaling Laws?
by: Lin, Haowei, et al.
Published: (2025)

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024)

Sequence-level Large Language Model Training with Contrastive Preference Optimization
by: Feng, Zhili, et al.
Published: (2025)

MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies
by: Zhang, Shiyue, et al.
Published: (2023)

Scaling Data-Constrained Language Models
by: Muennighoff, Niklas, et al.
Published: (2023)

Hypothesis Generation and Inductive Inference in Children and Language Models
by: Qin, Jeffrey, et al.
Published: (2026)

Investigating Annotator Bias in Large Language Models for Hate Speech Detection
by: Das, Amit, et al.
Published: (2024)

Eliciting Language Model Behaviors with Investigator Agents
by: Li, Xiang Lisa, et al.
Published: (2025)

Towards Trustable Language Models: Investigating Information Quality of Large Language Models
by: Rejeleene, Rick, et al.
Published: (2024)

Scaling Embeddings Outperforms Scaling Experts in Language Models
by: Liu, Hong, et al.
Published: (2026)

Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts
by: Lee, Sang-Woo, et al.
Published: (2025)

Rethinking Training Dynamics in Scale-wise Autoregressive Generation
by: Zhou, Gengze, et al.
Published: (2025)

Scaling Sparse Fine-Tuning to Large Language Models
by: Ansell, Alan, et al.
Published: (2024)

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
by: He, Liulu, et al.
Published: (2025)

HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models
by: Liu, Emmy, et al.
Published: (2026)

Large Scale Transfer Learning for Tabular Data via Language Modeling
by: Gardner, Josh, et al.
Published: (2024)

Regurgitative Training: The Value of Real Data in Training Large Language Models
by: Zhang, Jinghui, et al.
Published: (2024)

Investigating Data Contamination for Pre-training Language Models
by: Jiang, Minhao, et al.
Published: (2024)

Scaling Inference-Efficient Language Models
by: Bian, Song, et al.
Published: (2025)

Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
by: Feng, Xidong, et al.
Published: (2023)

Training Language Models to Explain Their Own Computations
by: Li, Belinda Z., et al.
Published: (2025)