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# Who cites «TinyStories: How Small Can Language Models Be and Still Speak Coherent English?»

158 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/eldan-2023-tinystories-small-language-models-still/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/eldan-2023-tinystories-small-language-models-still-2/)

1. [A Unified Assessment of the Poverty of the Stimulus Argument for Neural Language Models](https://arxiv.org/abs/2602.09992) *— 2026 · arXiv:2602.09992*
2. [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://aclanthology.org/2026.eacl-long.152/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Evaluating Small Decoder-Only Language Models for Grammar Correction and Text Simplification](https://arxiv.org/abs/2601.03874) *— 2026 · arXiv:2601.03874*
4. [Gaperon: A Peppered English-French Generative Language Model Suite](https://aclanthology.org/2026.findings-acl.1955/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
5. [HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://aclanthology.org/2026.findings-acl.1880/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [Making Synthetic Questions More Child-Directed: Prompting and Sampling Effects](https://aclanthology.org/2026.cdl-1.18/) *— 2026 · Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)*
7. [Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs](https://aclanthology.org/2026.acl-long.1688/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Nepali Legal Expertise through Generative and Extractive Pre-trained Transformers (NepLEGiT)](https://arxiv.org/abs/2609.16010) *— 2026 · arXiv:2609.16010*
9. [SteuerLLM: Local specialized large language model for German tax law analysis](https://arxiv.org/abs/2602.11081) *— 2026 · arXiv:2602.11081 · Scientific Reports, Volume 16, 2026*
10. [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://aclanthology.org/2025.emnlp-main.293/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
11. [Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development](https://aclanthology.org/2025.babylm-main.36/) *— 2025 · Proceedings of the First BabyLM Workshop*
12. [CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation](https://aclanthology.org/2025.tacl-1.76/) *— 2025 · Transactions of the Association for Computational Linguistics*
13. [Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling](https://aclanthology.org/2025.babylm-main.2/) *— 2025 · Proceedings of the First BabyLM Workshop*
14. [CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models](https://aclanthology.org/2025.babylm-main.20/) *— 2025 · Proceedings of the First BabyLM Workshop*
15. [Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models](https://aclanthology.org/2025.findings-naacl.87/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
16. [Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls](https://aclanthology.org/2025.emnlp-main.544/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
17. [Do Construction Distributions Shape Formal Language Learning In German BabyLMs?](https://aclanthology.org/2025.conll-1.12/) *— 2025 · Proceedings of the 29th Conference on Computational Natural Language Learning*
18. [Evaluating Synthetic Data Generation from User Generated Text](https://aclanthology.org/2025.cl-1.6/) *— 2025 · Computational Linguistics*
19. [FastDraft: How to Train Your Draft](https://aclanthology.org/2025.findings-acl.1156/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
20. [Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards](https://aclanthology.org/2025.emnlp-main.868/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
21. [Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems](https://aclanthology.org/2025.coling-industry.4/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics: Industry Track*
22. [Language Modeling by Language Models](https://arxiv.org/abs/2506.20249) *— 2025 · arXiv:2506.20249*
23. [MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation](https://aclanthology.org/2025.findings-acl.962/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
24. [Multilingual Language Model Pretraining using Machine-translated Data](https://aclanthology.org/2025.emnlp-main.1426/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
25. [Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://aclanthology.org/2025.acl-long.123/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Once Upon a Time: Interactive Learning for Storytelling with Small Language Models](https://aclanthology.org/2025.babylm-main.32/) *— 2025 · Proceedings of the First BabyLM Workshop*
27. [Pico: A Modular Framework for Hypothesis-Driven Small Language Model Research](https://arxiv.org/abs/2509.16413) *— 2025 · arXiv:2509.16413*
28. [RecombiText: Compositional Data Augmentation for Enhancing LLM Pre-Training Datasets in Low-Resource Scenarios](https://aclanthology.org/2025.babylm-main.40/) *— 2025 · Proceedings of the First BabyLM Workshop*
29. [Sample-Efficient Language Modeling with Linear Attention and Lightweight Enhancements](https://aclanthology.org/2025.babylm-main.14/) *— 2025 · Proceedings of the First BabyLM Workshop*
30. [Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs](https://aclanthology.org/2025.coling-main.437/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
31. [SpeechWeave: Diverse Multilingual Synthetic Text & Audio Data Generation Pipeline for Training Text to Speech Models](https://aclanthology.org/2025.acl-industry.51/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
32. [Syntriever: How to Train Your Retriever with Synthetic Data from LLMs](https://aclanthology.org/2025.findings-naacl.136/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
33. [TinyServe: Query-Aware Cache Selection for Efficient LLM Serving](https://arxiv.org/abs/2509.12211) *— 2025 · arXiv:2509.12211*
34. [Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs](https://aclanthology.org/2025.coling-main.79/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
35. [Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models](https://aclanthology.org/2025.babylm-main.3/) *— 2025 · Proceedings of the First BabyLM Workshop*
36. [A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://arxiv.org/abs/2411.03350) *— 2024 · arXiv:2411.03350*
37. [A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia](https://aclanthology.org/2024.acl-long.369/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [Arabic Automatic Story Generation with Large Language Models](https://aclanthology.org/2024.arabicnlp-1.13/) *— 2024 · Proceedings of the Second Arabic Natural Language Processing Conference*
39. [BERTtime Stories: Investigating the Role of Synthetic Story Data in Language Pre-training](https://aclanthology.org/2024.conll-babylm.28/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
40. [CUTE: Measuring LLMs'' Understanding of Their Tokens](https://aclanthology.org/2024.emnlp-main.177/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
41. [DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts](https://aclanthology.org/2024.emnlp-main.1073/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
42. [DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models](https://aclanthology.org/2024.emnlp-main.83/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
43. [Emergent Abilities in Reduced-Scale Generative Language Models](https://aclanthology.org/2024.findings-naacl.79/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
44. [Event Causality Identification with Synthetic Control](https://aclanthology.org/2024.emnlp-main.103/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
45. [Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice](https://arxiv.org/abs/2409.07713) *— 2024 · arXiv:2409.07713*
46. [Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2024.conll-babylm.1/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
47. [From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation](https://aclanthology.org/2024.findings-acl.926/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
48. [From fieldwork to linguistic theory: A tribute to Dan Everett](https://doi.org/10.5281/zenodo.11351540) *— 2024 · edited-volume*
49. [HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level Synthesis](https://arxiv.org/abs/2405.00738) *— 2024 · arXiv:2405.00738*
50. [Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis](https://aclanthology.org/2024.arabicnlp-1.7/) *— 2024 · Proceedings of the Second Arabic Natural Language Processing Conference*
51. [InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://aclanthology.org/2024.findings-acl.772/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
52. [Is Child-Directed Speech Effective Training Data for Language Models?](https://aclanthology.org/2024.emnlp-main.1231/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
53. [Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?](https://arxiv.org/abs/2411.15821) *— 2024 · arXiv:2411.15821 · Commun. Comput. Inf. Sci. 2665 (2025) 75-87*
54. [Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs](https://aclanthology.org/2024.emnlp-main.53/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
55. [Language Models as Models of Language](https://arxiv.org/abs/2408.07144) *— 2024 · arXiv:2408.07144*
56. [Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models](https://arxiv.org/abs/2411.00686) *— 2024 · arXiv:2411.00686*
57. [MIND: Math Informed syNthetic Dialogues for Pretraining LLMs](https://arxiv.org/abs/2410.12881) *— 2024 · arXiv:2410.12881*
58. [Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures](https://arxiv.org/abs/2407.19580) *— 2024 · arXiv:2407.19580*
59. [MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://aclanthology.org/2024.acl-long.117/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://aclanthology.org/2024.findings-acl.658/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
61. [On the Diversity of Synthetic Data and its Impact on Training Large Language Models](https://arxiv.org/abs/2410.15226) *— 2024 · arXiv:2410.15226*
62. [OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset](https://arxiv.org/abs/2402.10176) *— 2024 · arXiv:2402.10176*
63. [Pretraining Language Models Using Translationese](https://aclanthology.org/2024.emnlp-main.334/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
64. [Reason from Fallacy: Enhancing Large Language Models'' Logical Reasoning through Logical Fallacy Understanding](https://aclanthology.org/2024.findings-naacl.192/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
65. [Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://aclanthology.org/2024.acl-long.757/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [Scaling Properties of Speech Language Models](https://aclanthology.org/2024.emnlp-main.21/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
67. [Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification](https://aclanthology.org/2024.lrec-main.1299/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
68. [Small Language Models: Survey, Measurements, and Insights](https://arxiv.org/abs/2409.15790) *— 2024 · arXiv:2409.15790*
69. [Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models](https://arxiv.org/abs/2412.02980) *— 2024 · arXiv:2412.02980*
70. [Synthetic continued pretraining](https://arxiv.org/abs/2409.07431) *— 2024 · arXiv:2409.07431*
71. [The Fourth Evaluation on Chinese Spatial Cognition](https://aclanthology.org/2024.ccl-3.14/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)*
72. [The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks](https://aclanthology.org/2024.eacl-short.17/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
73. [The Unreasonable Ineffectiveness of the Deeper Layers](https://arxiv.org/abs/2403.17887) *— 2024 · arXiv:2403.17887*
74. [Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding](https://arxiv.org/abs/2402.16844) *— 2024 · arXiv:2402.16844*
75. [Towards Pareto Optimal Throughput in Small Language Model Serving](https://arxiv.org/abs/2404.03353) *— 2024 · arXiv:2404.03353*
76. [WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset](https://arxiv.org/abs/2402.19282) *— 2024 · arXiv:2402.19282*
77. [What Evidence Do Language Models Find Convincing?](https://aclanthology.org/2024.acl-long.403/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [What should Baby Models read? Exploring Sample-Efficient Data Composition on Model Performance](https://aclanthology.org/2024.conll-babylm.25/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
79. [WhatIf: Leveraging Word Vectors for Small-Scale Data Augmentation](https://aclanthology.org/2024.conll-babylm.20/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
80. [小規模言語モデルによる子供の過剰一般化のモデリング](https://www.anlp.jp/proceedings/annual_meeting/2024/#E9-1) *— 2024 · 言語処理学会 第30回年次大会 発表論文集 (NLP2024)*
81. [BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?](https://aclanthology.org/2023.conll-babylm.16/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
82. [Efficient RLHF: Reducing the Memory Usage of PPO](https://arxiv.org/abs/2309.00754) *— 2023 · arXiv:2309.00754*
83. [Exposing Attention Glitches with Flip-Flop Language Modeling](https://arxiv.org/abs/2306.00946) *— 2023 · arXiv:2306.00946*
84. [Gentopia: A Collaborative Platform for Tool-Augmented LLMs](https://arxiv.org/abs/2308.04030) *— 2023 · arXiv:2308.04030*
85. [Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures](https://aclanthology.org/2023.conll-babylm.12/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
86. [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://arxiv.org/abs/2309.12284) *— 2023 · arXiv:2309.12284*
87. [Mini Minds: Exploring Bebeshka and Zlata Baby Models](https://aclanthology.org/2023.conll-babylm.4/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
88. [Physics of Language Models: Part 3.1, Knowledge Storage and Extraction](https://arxiv.org/abs/2309.14316) *— 2023 · arXiv:2309.14316*
89. [Pretraining on the Test Set Is All You Need](https://arxiv.org/abs/2309.08632) *— 2023 · arXiv:2309.08632*
90. [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) *— 2023 · arXiv:2306.11644*
91. [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463) *— 2023 · arXiv:2309.05463*
92. [The Quantization Model of Neural Scaling](https://arxiv.org/abs/2303.13506) *— 2023 · arXiv:2303.13506*
93. [TinyGSM: achieving >80% on GSM8k with small language models](https://arxiv.org/abs/2312.09241) *— 2023 · arXiv:2312.09241*
94. [ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding](https://aclanthology.org/2023.conll-babylm.14/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
95. [``Fifty Shades of Bias'''': Normative Ratings of Gender Bias in GPT Generated English Text](https://aclanthology.org/2023.emnlp-main.115/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
96. alyafeai-2025-mole-metadata-extraction-validation-scientific
97. baker-2025-structural-inference-interpreting-small-language
98. bergeron-2024-blu-syntra-distinguish-synergies-trade
99. bian-2024-rule-or-story-which-is
100. brown-2025-pula-training-large-language-models

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