{"total":144,"rows":[{"id":"2306.01116","title":"The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only","created":"2023-06-01","cited_by_count":763,"area":null},{"id":"2211.04325","title":"Will we run out of data? Limits of LLM scaling based on human-generated data","created":"2022-10-26","cited_by_count":195,"area":null},{"id":"2310.03003","title":"From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference","created":"2023-10-04","cited_by_count":170,"area":null},{"id":"2503.14499","title":"Measuring AI Ability to Complete Long Software Tasks","created":"2025-03-18","cited_by_count":156,"area":null},{"id":"2302.08500","title":"Auditing large language models: a three-layered approach","created":"2023-02-16","cited_by_count":137,"area":null},{"id":"2207.13243","title":"Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks","created":"2022-07-27","cited_by_count":129,"area":null},{"id":"2307.02028","title":"EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models","created":"2023-07-05","cited_by_count":105,"area":null},{"id":"2307.03718","title":"Frontier AI Regulation: Managing Emerging Risks to Public Safety","created":"2023-07-06","cited_by_count":98,"area":null},{"id":"2403.18421","title":"BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text","created":"2024-03-27","cited_by_count":86,"area":null},{"id":"2304.00612","title":"Eight Things to Know about Large Language Models","created":"2023-04-02","cited_by_count":73,"area":null},{"id":"2305.06972","title":"Spear Phishing With Large Language Models","created":"2023-05-11","cited_by_count":52,"area":null},{"id":"2302.08469","title":"Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators","created":"2023-02-16","cited_by_count":43,"area":null},{"id":"2207.02852","title":"Machine Learning Model Sizes and the Parameter Gap","created":"2022-07-05","cited_by_count":39,"area":null},{"id":"2207.14382","title":"Large Language Models and the Reverse Turing Test","created":"2022-07-28","cited_by_count":38,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2311.09227","title":"Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives","created":"2023-09-29","cited_by_count":31,"area":null},{"id":"2304.13714","title":"Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery","created":"2023-04-26","cited_by_count":30,"area":null},{"id":"2409.12903","title":"Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization","created":"2024-09-19","cited_by_count":29,"area":null},{"id":"2305.03123","title":"ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review","created":"2023-04-13","cited_by_count":28,"area":null},{"id":"2304.04640","title":"NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems","created":"2023-04-10","cited_by_count":27,"area":null},{"id":"2312.00333","title":"Green Edge AI: A Contemporary Survey","created":"2023-12-01","cited_by_count":25,"area":null},{"id":"2401.04491","title":"SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning","created":"2024-01-09","cited_by_count":25,"area":null},{"id":"2303.11341","title":"What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring","created":"2023-03-20","cited_by_count":21,"area":null},{"id":"2311.08391","title":"A Material Lens on Coloniality in NLP","created":"2023-11-14","cited_by_count":21,"area":null},{"id":"2601.22813","title":"Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation","created":"2026-01-30","cited_by_count":21,"area":null},{"id":"2401.02452","title":"The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?","created":"2024-01-04","cited_by_count":20,"area":null},{"id":"2205.05071","title":"Towards Climate Awareness in NLP Research","created":"2022-05-10","cited_by_count":19,"area":null},{"id":"2203.02202","title":"Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis","created":"2022-03-04","cited_by_count":18,"area":null},{"id":"2407.05694","title":"On the Limitations of Compute Thresholds as a Governance Strategy","created":"2024-07-08","cited_by_count":18,"area":null},{"id":"2211.03796","title":"Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy","created":"2022-11-07","cited_by_count":17,"area":null},{"id":"2302.10916","title":"How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study","created":"2023-02-17","cited_by_count":16,"area":null},{"id":"2410.12032","title":"MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI","created":"2024-10-15","cited_by_count":16,"area":null},{"id":"2411.11504","title":"Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering","created":"2024-11-18","cited_by_count":16,"area":null},{"id":"2302.08545","title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression","created":"2023-02-16","cited_by_count":15,"area":null},{"id":"2311.01550","title":"Market Concentration Implications of Foundation Models","created":"2023-11-02","cited_by_count":15,"area":null},{"id":"2402.01909","title":"On Catastrophic Inheritance of Large Foundation Models","created":"2024-02-02","cited_by_count":15,"area":null},{"id":"2405.10799","title":"Training Compute Thresholds: Features and Functions in AI Regulation","created":"2024-05-17","cited_by_count":14,"area":null},{"id":"2310.10046","title":"TRANSOM: An Efficient Fault-Tolerant System for Training LLMs","created":"2023-10-16","cited_by_count":13,"area":null},{"id":"2403.12107","title":"Scenarios for the Transition to AGI","created":"2024-03-17","cited_by_count":13,"area":null},{"id":"2308.03407","title":"Spatially Varying Nanophotonic Neural Networks","created":"2023-08-07","cited_by_count":12,"area":null},{"id":"2309.02065","title":"Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI","created":"2023-09-05","cited_by_count":12,"area":null},{"id":"2210.06015","title":"EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search","created":"2022-10-12","cited_by_count":11,"area":null},{"id":"2204.02804","title":"Federated Self-supervised Speech Representations: Are We There Yet?","created":"2022-04-06","cited_by_count":10,"area":null},{"id":"2204.06251","title":"Experimental Standards for Deep Learning in Natural Language Processing Research","created":"2022-04-13","cited_by_count":10,"area":null},{"id":"2302.10360","title":"Optical Transformers","created":"2023-02-20","cited_by_count":10,"area":null},{"id":"2409.11416","title":"The Unseen AI Disruptions for Power Grids: LLM-Induced Transients","created":"2024-09-09","cited_by_count":10,"area":null},{"id":"2404.15786","title":"Rethinking model prototyping through the MedMNIST+ dataset collection","created":"2024-04-24","cited_by_count":9,"area":null},{"id":"2405.06258","title":"Automatic Generation of Model and Data Cards: A Step Towards Responsible AI","created":"2024-05-10","cited_by_count":9,"area":null},{"id":"2405.14930","title":"AstroPT: Scaling Large Observation Models for Astronomy","created":"2024-05-23","cited_by_count":9,"area":null},{"id":"2603.03992","title":"Measuring AI R&D Automation","created":"2026-03-04","cited_by_count":9,"area":null},{"id":"2303.14604","title":"Green Federated Learning","created":"2023-03-26","cited_by_count":8,"area":null},{"id":"2305.19011","title":"MiniSUPERB: Lightweight Benchmark for Self-supervised Speech Models","created":"2023-05-30","cited_by_count":8,"area":null},{"id":"2308.15514","title":"International Governance of Civilian AI: A Jurisdictional Certification Approach","created":"2023-08-29","cited_by_count":8,"area":null},{"id":"2309.06054","title":"Breaking through the learning plateaus of in-context learning in Transformer","created":"2023-09-12","cited_by_count":8,"area":null},{"id":"2509.22092","title":"Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements","created":"2025-09-26","cited_by_count":8,"area":null},{"id":"2206.02841","title":"Researching Alignment Research: Unsupervised Analysis","created":"2022-06-06","cited_by_count":7,"area":null},{"id":"2403.03699","title":"Model Parallelism on Distributed Infrastructure: A Literature Review from Theory to LLM Case-Studies","created":"2024-03-06","cited_by_count":7,"area":null},{"id":"2210.12924","title":"OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks","created":"2022-10-24","cited_by_count":6,"area":null},{"id":"2303.10181","title":"Operating critical machine learning models in resource constrained regimes","created":"2023-03-17","cited_by_count":6,"area":null},{"id":"2306.08888","title":"ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design","created":"2023-06-15","cited_by_count":6,"area":null},{"id":"2411.06346","title":"Activation Map Compression through Tensor Decomposition for Deep Learning","created":"2024-11-10","cited_by_count":6,"area":null},{"id":"2301.11581","title":"A Green(er) World for A.I","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2411.02643","title":"A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers","created":"2024-11-04","cited_by_count":5,"area":null},{"id":"2507.06173","title":"A Method for Optimizing Connections in Differentiable Logic Gate Networks","created":"2025-07-08","cited_by_count":5,"area":null},{"id":"2511.21739","title":"Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers","created":"2025-11-21","cited_by_count":5,"area":null},{"id":"2307.05520","title":"Estimating Deep Learning energy consumption based on model architecture and training environment","created":"2023-07-07","cited_by_count":4,"area":null},{"id":"2309.11856","title":"Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance Minimization","created":"2023-09-21","cited_by_count":4,"area":null},{"id":"2311.05020","title":"First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models","created":"2023-11-08","cited_by_count":4,"area":null},{"id":"2402.07529","title":"Accelerating Distributed Deep Learning using Lossless Homomorphic Compression","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2409.05080","title":"From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems","created":"2024-09-08","cited_by_count":4,"area":null},{"id":"2411.12820","title":"Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation","created":"2024-11-19","cited_by_count":4,"area":null},{"id":"2606.12683","title":"From AGI to ASI","created":"2026-06-10","cited_by_count":4,"area":null},{"id":"2303.16214","title":"Tetra-AML: Automatic Machine Learning via Tensor Networks","created":"2023-03-28","cited_by_count":3,"area":null},{"id":"2311.00382","title":"Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2406.01981","title":"Zyda: A 1.3T Dataset for Open Language Modeling","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2506.17347","title":"Distinguishing Task-Specific and General-Purpose AI in Regulation","created":"2025-06-19","cited_by_count":3,"area":null},{"id":"2308.08276","title":"Computer vision-enriched discrete choice models, with an application to residential location choice","created":"2023-08-16","cited_by_count":2,"area":null},{"id":"2403.06497","title":"QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2409.08763","title":"Energy Consumption Trends in Sound Event Detection Systems","created":"2024-09-13","cited_by_count":2,"area":null},{"id":"2412.17376","title":"How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts","created":"2024-12-23","cited_by_count":2,"area":null},{"id":"2501.03840","title":"Machine learning applications in archaeological practices: a review","created":"2025-01-07","cited_by_count":2,"area":null},{"id":"2501.17980","title":"Limits to AI Growth: The Ecological and Social Consequences of Scaling","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2502.20016","title":"Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race","created":"2025-02-27","cited_by_count":2,"area":null},{"id":"2506.22771","title":"FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision","created":"2025-06-28","cited_by_count":2,"area":null},{"id":"2507.01803","title":"Towards Decentralized and Sustainable Foundation Model Training with the Edge","created":"2025-07-02","cited_by_count":2,"area":null},{"id":"2507.07931","title":"Meek Models Shall Inherit the Earth","created":"2025-07-10","cited_by_count":2,"area":null},{"id":"2509.24257","title":"VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2510.09022","title":"The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2607.09399","title":"Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks","created":"2026-07-10","cited_by_count":2,"area":null},{"id":"2607.25271","title":"Bridging Compute- and Data-Optimal Pretraining","created":"2026-07-28","cited_by_count":2,"area":null},{"id":"2304.07613","title":"STen: Productive and Efficient Sparsity in PyTorch","created":"2023-04-15","cited_by_count":1,"area":null},{"id":"2305.17300","title":"Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence","created":"2023-05-26","cited_by_count":1,"area":null},{"id":"2310.07516","title":"Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining","created":"2023-10-11","cited_by_count":1,"area":null},{"id":"2310.09217","title":"Multinational AGI Consortium (MAGIC): A Proposal for International Coordination on AI","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2402.05675","title":"Is Adversarial Training with Compressed Datasets Effective?","created":"2024-02-08","cited_by_count":1,"area":null},{"id":"2403.09441","title":"An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks","created":"2024-03-14","cited_by_count":1,"area":null},{"id":"2409.05602","title":"Normalizing Energy Consumption for Hardware-Independent Evaluation","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2410.16227","title":"Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2502.00490","title":"Oscillations Make Neural Networks Robust to Quantization","created":"2025-02-01","cited_by_count":1,"area":null},{"id":"2503.22451","title":"STADE: Standard Deviation as a Pruning Metric","created":"2025-03-28","cited_by_count":1,"area":null}],"next_cursor":"100"}