{"total":62,"rows":[{"id":"2310.03302","title":"MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation","created":"2023-10-05","cited_by_count":366,"area":"agents"},{"id":"2504.08165","title":"Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora","created":"2025-04-10","cited_by_count":88,"area":null},{"id":"2304.08442","title":"The MiniPile Challenge for Data-Efficient Language Models","created":"2023-04-17","cited_by_count":75,"area":null},{"id":"2309.16575","title":"A Benchmark for Learning to Translate a New Language from One Grammar Book","created":"2023-09-28","cited_by_count":75,"area":null},{"id":"2308.11696","title":"Efficient Benchmarking of Language Models","created":"2023-08-22","cited_by_count":71,"area":null},{"id":"2308.02019","title":"Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty","created":"2023-08-03","cited_by_count":57,"area":"distill"},{"id":"2304.13060","title":"Injecting structural hints: Using language models to study inductive biases in language learning","created":"2023-04-25","cited_by_count":35,"area":null},{"id":"2401.06416","title":"Mission: Impossible Language Models","created":"2024-01-12","cited_by_count":32,"area":null},{"id":"2306.01506","title":"BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models","created":"2023-06-02","cited_by_count":29,"area":null},{"id":"2509.14786","title":"Pre-training under infinite compute","created":"2025-09-18","cited_by_count":29,"area":null},{"id":"2404.06214","title":"[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus","created":"2024-04-09","cited_by_count":26,"area":null},{"id":"2308.00189","title":"Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?","created":"2023-07-31","cited_by_count":19,"area":null},{"id":"2411.07464","title":"BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks","created":"2024-11-12","cited_by_count":19,"area":"agents"},{"id":"2310.13257","title":"Visual Grounding Helps Learn Word Meanings in Low-Data Regimes","created":"2023-10-20","cited_by_count":17,"area":null},{"id":"2504.12427","title":"Position: The Most Expensive Part of an LLM should be its Training Data","created":"2025-04-16","cited_by_count":16,"area":null},{"id":"2402.05785","title":"Limits of Transformer Language Models on Learning to Compose Algorithms","created":"2024-02-08","cited_by_count":13,"area":null},{"id":"2402.12363","title":"Emergent Word Order Universals from Cognitively-Motivated Language Models","created":"2024-02-19","cited_by_count":11,"area":null},{"id":"2402.16844","title":"Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding","created":"2024-02-26","cited_by_count":11,"area":"lr"},{"id":"2410.22906","title":"From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes","created":"2024-10-30","cited_by_count":11,"area":null},{"id":"2510.04764","title":"Are BabyLMs Deaf to Gricean Maxims? A Pragmatic Evaluation of Sample-efficient Language Models","created":"2025-10-06","cited_by_count":11,"area":null},{"id":"2307.04251","title":"ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey","created":"2023-07-09","cited_by_count":10,"area":null},{"id":"2308.01684","title":"Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models","created":"2023-08-03","cited_by_count":9,"area":"lr"},{"id":"2412.03275","title":"AntLM: Bridging Causal and Masked Language Models","created":"2024-12-04","cited_by_count":8,"area":null},{"id":"2404.12145","title":"From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency","created":"2024-04-18","cited_by_count":7,"area":null},{"id":"2405.15164","title":"From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks","created":"2024-05-24","cited_by_count":7,"area":null},{"id":"2603.18534","title":"Data-efficient pre-training by scaling synthetic megadocs","created":"2026-03-19","cited_by_count":7,"area":null},{"id":"2504.03036","title":"IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling","created":"2025-04-03","cited_by_count":6,"area":null},{"id":"2504.09426","title":"BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning","created":"2025-04-13","cited_by_count":6,"area":"lr"},{"id":"2308.09720","title":"On the Unexpected Abilities of Large Language Models","created":"2023-08-09","cited_by_count":5,"area":null},{"id":"2407.17356","title":"Gradient-based inference of abstract task representations for generalization in neural networks","created":"2024-07-24","cited_by_count":4,"area":null},{"id":"2505.05970","title":"Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models","created":"2025-05-09","cited_by_count":4,"area":null},{"id":"2410.15365","title":"BERTtime Stories: Investigating the Role of Synthetic Story Data in Language Pre-training","created":"2024-10-20","cited_by_count":3,"area":null},{"id":"2502.17910","title":"Scaling LLM Pre-training with Vocabulary Curriculum","created":"2025-02-25","cited_by_count":3,"area":null},{"id":"2510.04268","title":"LongTail-Swap: benchmarking language models' abilities on rare words","created":"2025-10-05","cited_by_count":3,"area":null},{"id":"2312.02931","title":"WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words","created":"2023-12-05","cited_by_count":2,"area":null},{"id":"2403.10338","title":"Investigating grammatical abstraction in language models using few-shot learning of novel noun gender","created":"2024-03-15","cited_by_count":2,"area":null},{"id":"2410.21332","title":"Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences","created":"2024-10-27","cited_by_count":2,"area":null},{"id":"2504.03338","title":"BabyLM's First Words: Word Segmentation as a Phonological Probing Task","created":"2025-04-04","cited_by_count":2,"area":null},{"id":"2504.08590","title":"Playpen: An Environment for Exploring Learning Through Conversational Interaction","created":"2025-04-11","cited_by_count":2,"area":"alignment"},{"id":"2508.15475","title":"Influence-driven Curriculum Learning for Pre-training on Limited Data","created":"2025-08-21","cited_by_count":2,"area":null},{"id":"2509.05056","title":"Masked Diffusion Language Models with Frequency-Informed Training","created":"2025-09-05","cited_by_count":2,"area":"diffusion"},{"id":"2510.08470","title":"Looking to Learn: Token-wise Dynamic Gating for Low-Resource Vision-Language Modelling","created":"2025-10-09","cited_by_count":2,"area":"mllm"},{"id":"2604.10333","title":"Zero-shot World Models Are Developmentally Efficient Learners","created":"2026-04-11","cited_by_count":2,"area":"wm"},{"id":"2308.16336","title":"ToddlerBERTa: Exploiting BabyBERTa for Grammar Learning and Language Understanding","created":"2023-08-30","cited_by_count":1,"area":null},{"id":"2504.07989","title":"Regional Tiny Stories: Using Small Models to Compare Language Learning and Tokenizer Performance","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2311.07498","title":"Reducing the Need for Backpropagation and Discovering Better Optima With Explicit Optimizations of Neural Networks","created":"2023-11-13","cited_by_count":0,"area":null},{"id":"2311.07510","title":"Explicit Foundation Model Optimization with Self-Attentive Feed-Forward Neural Units","created":"2023-11-13","cited_by_count":0,"area":null},{"id":"2311.11012","title":"Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models","created":"2023-11-18","cited_by_count":0,"area":null},{"id":"2312.06881","title":"DYAD: A Descriptive Yet Abjuring Density efficient approximation to linear neural network layers","created":"2023-12-11","cited_by_count":0,"area":null},{"id":"2410.12391","title":"Tracking Universal Features Through Fine-Tuning and Model Merging","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2412.03098","title":"A surprisal oracle for when every layer counts","created":"2024-12-04","cited_by_count":0,"area":null},{"id":"2503.01724","title":"Syntactic Learnability of Echo State Neural Language Models at Scale","created":"2025-03-03","cited_by_count":0,"area":null},{"id":"2504.21047","title":"Model Connectomes: A Generational Approach to Data-Efficient Language Models","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2505.16277","title":"Spontaneous Speech Variables for Evaluating LLMs Cognitive Plausibility","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2507.19741","title":"Basic Reading Distillation","created":"2025-07-26","cited_by_count":0,"area":"distill"},{"id":"2508.11598","title":"Representing Speech Through Autoregressive Prediction of Cochlear Tokens","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2606.05497","title":"LEVANTE-bench: Multi-Scale Comparison of VLMs to Children Using Cognitive Tasks (or, \"Is Your VLM Smarter Than a 5th Grader?\")","created":"2026-06-03","cited_by_count":0,"area":"mllm"},{"id":"2606.10467","title":"Large Language Models as Modal Models in Linguistics","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2607.10268","title":"Language Re-generation: An investigation into information locality effects on reconstruction","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2608.13545","title":"LittleLearner: Language Models Under Pedagogically Controlled Knowledge Exposure","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2608.22452","title":"From Exposure to Expectation: Frequency, Surprisal, and Language Across Development in Spanish","created":"2026-08-23","cited_by_count":0,"area":null},{"id":"2609.26539","title":"A retrospective analysis on the use of LLMs to study infant syntax learning","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}