Who cites «TinyStories: How Small Can Language Models Be and Still Speak Coherent English?» · page 2

158 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

From fieldwork to linguistic theory: A tribute to Dan Everett — 2024 · edited-volume
HLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level Synthesis — 2024 · arXiv:2405.00738
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis — 2024 · Proceedings of the Second Arabic Natural Language Processing Conference
InFoBench: Evaluating Instruction Following Ability in Large Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
Information-Theoretic Distillation for Reference-less Summarization — 2024 · arXiv:2403.13780
Is Child-Directed Speech Effective Training Data for Language Models? — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Is Training Data Quality or Quantity More Impactful to Small Language Model Performance? — 2024 · arXiv:2411.15821 · Commun. Comput. Inf. Sci. 2665 (2025) 75-87
Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Language Models as Models of Language — 2024 · arXiv:2408.07144
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models — 2024 · arXiv:2411.00686
MIND: Math Informed syNthetic Dialogues for Pretraining LLMs — 2024 · arXiv:2410.12881
Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures — 2024 · arXiv:2407.19580
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Modeling Overregularization in Children with Small Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
On the Diversity of Synthetic Data and its Impact on Training Large Language Models — 2024 · arXiv:2410.15226
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset — 2024 · arXiv:2402.10176
Pretraining Language Models Using Translationese — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Reason from Fallacy: Enhancing Large Language Models'' Logical Reasoning through Logical Fallacy Understanding — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
Rephrasing Electronic Health Records for Pretraining Clinical Language Models — 2024 · Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association
Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models? — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Scaling Properties of Speech Language Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification — 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Small Language Models: Survey, Measurements, and Insights — 2024 · arXiv:2409.15790
SpeciaLex: A Benchmark for In-Context Specialized Lexicon Learning — 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models — 2024 · arXiv:2412.02980
Synthetic continued pretraining — 2024 · arXiv:2409.07431
The Fourth Evaluation on Chinese Spatial Cognition — 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks — 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
The Unreasonable Ineffectiveness of the Deeper Layers — 2024 · arXiv:2403.17887
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding — 2024 · arXiv:2402.16844
Towards Pareto Optimal Throughput in Small Language Model Serving — 2024 · arXiv:2404.03353
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Visual Editing with LLM-based Tool Chaining: An Efficient Distillation Approach for Real-Time Applications — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset — 2024 · arXiv:2402.19282
What Evidence Do Language Models Find Convincing? — 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
What should Baby Models read? Exploring Sample-Efficient Data Composition on Model Performance — 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning
WhatIf: Leveraging Word Vectors for Small-Scale Data Augmentation — 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning
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 — 2024 · 言語処理学会第30回年次大会発表論文集 (NLP2024)
BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories? — 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
DYAD: A Descriptive Yet Abjuring Density efficient approximation to linear neural network layers — 2023 · arXiv:2312.06881
Efficient RLHF: Reducing the Memory Usage of PPO — 2023 · arXiv:2309.00754
Exposing Attention Glitches with Flip-Flop Language Modeling — 2023 · arXiv:2306.00946
Gentopia.AI: A Collaborative Platform for Tool-Augmented LLMs — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Gentopia: A Collaborative Platform for Tool-Augmented LLMs — 2023 · arXiv:2308.04030
Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures — 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models — 2023 · arXiv:2309.12284
Mini Minds: Exploring Bebeshka and Zlata Baby Models — 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
Physics of Language Models: Part 3.1, Knowledge Storage and Extraction — 2023 · arXiv:2309.14316
Pretraining on the Test Set Is All You Need — 2023 · arXiv:2309.08632
Textbooks Are All You Need — 2023 · arXiv:2306.11644
Textbooks Are All You Need II: phi-1.5 technical report — 2023 · arXiv:2309.05463
The Quantization Model of Neural Scaling — 2023 · arXiv:2303.13506
TinyGSM: achieving >80% on GSM8k with small language models — 2023 · arXiv:2312.09241
ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding — 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
``Fifty Shades of Bias'''': Normative Ratings of Gender Bias in GPT Generated English Text — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing