{"total":82,"rows":[{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326,"area":null},{"id":"2409.19256","title":"HybridFlow: A Flexible and Efficient RLHF Framework","created":"2024-09-28","cited_by_count":2057,"area":"alignment"},{"id":"2212.13138","title":"Large Language Models Encode Clinical Knowledge","created":"2022-12-26","cited_by_count":1830,"area":null},{"id":"2205.01917","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","created":"2022-05-04","cited_by_count":1274,"area":null},{"id":"2305.10403","title":"PaLM 2 Technical Report","created":"2023-05-17","cited_by_count":1162,"area":"lr"},{"id":"2212.03191","title":"InternVideo: General Video Foundation Models via Generative and Discriminative Learning","created":"2022-12-06","cited_by_count":447,"area":null},{"id":"2307.14334","title":"Towards Generalist Biomedical AI","created":"2023-07-26","cited_by_count":289,"area":null},{"id":"2303.06674","title":"Universal Instance Perception as Object Discovery and Retrieval","created":"2023-03-12","cited_by_count":192,"area":null},{"id":"2310.05694","title":"A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics","created":"2023-10-09","cited_by_count":163,"area":null},{"id":"2210.06313","title":"The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers","created":"2022-10-12","cited_by_count":122,"area":null},{"id":"2210.17223","title":"Accelerating Distributed MoE Training and Inference with Lina","created":"2022-10-31","cited_by_count":109,"area":"moe"},{"id":"2206.09557","title":"LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models","created":"2022-06-20","cited_by_count":105,"area":null},{"id":"2405.03162","title":"Advancing Multimodal Medical Capabilities of Gemini","created":"2024-05-06","cited_by_count":90,"area":null},{"id":"2209.00099","title":"Efficient Methods for Natural Language Processing: A Survey","created":"2022-08-31","cited_by_count":78,"area":null},{"id":"2211.09760","title":"VeLO: Training Versatile Learned Optimizers by Scaling Up","created":"2022-11-17","cited_by_count":57,"area":null},{"id":"2601.16208","title":"Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders","created":"2026-01-22","cited_by_count":53,"area":"diffusion"},{"id":"2210.12714","title":"Generative Knowledge Graph Construction: A Review","created":"2022-10-23","cited_by_count":49,"area":null},{"id":"2310.16795","title":"QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models","created":"2023-10-25","cited_by_count":42,"area":"moe"},{"id":"2210.10488","title":"Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective","created":"2022-10-19","cited_by_count":41,"area":null},{"id":"2402.08113","title":"Addressing cognitive bias in medical language models","created":"2024-02-12","cited_by_count":36,"area":null},{"id":"2508.15734","title":"Measuring the environmental impact of delivering AI at Google Scale","created":"2025-08-21","cited_by_count":32,"area":null},{"id":"2304.13169","title":"SAFE: Machine Unlearning With Shard Graphs","created":"2023-04-25","cited_by_count":28,"area":"safety"},{"id":"2410.21680","title":"Revisiting Reliability in Large-Scale Machine Learning Research Clusters","created":"2024-10-29","cited_by_count":28,"area":null},{"id":"2302.02596","title":"Ten Lessons We Have Learned in the New \"Sparseland\": A Short Handbook for Sparse Neural Network Researchers","created":"2023-02-06","cited_by_count":25,"area":null},{"id":"2406.06316","title":"Tx-LLM: A Large Language Model for Therapeutics","created":"2024-06-10","cited_by_count":25,"area":null},{"id":"2305.15498","title":"Large Language Models for User Interest Journeys","created":"2023-05-24","cited_by_count":24,"area":null},{"id":"2211.05322","title":"On Optimizing the Communication of Model Parallelism","created":"2022-11-10","cited_by_count":19,"area":null},{"id":"2212.04408","title":"OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models","created":"2022-12-08","cited_by_count":17,"area":null},{"id":"2205.12755","title":"An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2306.03715","title":"Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability","created":"2023-06-06","cited_by_count":16,"area":null},{"id":"2312.05181","title":"Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections","created":"2023-12-08","cited_by_count":16,"area":null},{"id":"2404.09101","title":"Mixtures of Neural Operators Reduce Active Complexity in Operator Learning","created":"2024-04-13","cited_by_count":16,"area":null},{"id":"2509.16293","title":"Robust LLM Training Infrastructure at ByteDance","created":"2025-09-19","cited_by_count":16,"area":null},{"id":"2211.08675","title":"XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse","created":"2022-11-16","cited_by_count":14,"area":null},{"id":"2303.12132","title":"Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense","created":"2023-03-21","cited_by_count":13,"area":null},{"id":"2205.04713","title":"Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures","created":"2022-05-10","cited_by_count":12,"area":null},{"id":"2405.17035","title":"Glauber Generative Model: Discrete Diffusion Models via Binary Classification","created":"2024-05-27","cited_by_count":11,"area":"diffusion"},{"id":"2410.15625","title":"Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces","created":"2024-10-21","cited_by_count":11,"area":null},{"id":"2503.06823","title":"eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference","created":"2025-03-10","cited_by_count":11,"area":"moe"},{"id":"2203.17090","title":"PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model","created":"2022-03-31","cited_by_count":9,"area":null},{"id":"2209.07326","title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","created":"2022-09-15","cited_by_count":9,"area":null},{"id":"2402.18789","title":"FlexLLM: Token-Level Co-Serving of LLM Inference and Finetuning with SLO Guarantees","created":"2024-02-29","cited_by_count":9,"area":null},{"id":"2403.07128","title":"DrJAX: Scalable and Differentiable MapReduce Primitives in JAX","created":"2024-03-11","cited_by_count":9,"area":null},{"id":"2409.15241","title":"Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping","created":"2024-09-23","cited_by_count":9,"area":null},{"id":"2309.09362","title":"Language models are susceptible to incorrect patient self-diagnosis in medical applications","created":"2023-09-17","cited_by_count":8,"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":"2301.04020","title":"Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence","created":"2022-12-13","cited_by_count":6,"area":null},{"id":"2502.17132","title":"Applications of Large Models in Medicine","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2301.08984","title":"SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction","created":"2023-01-21","cited_by_count":5,"area":null},{"id":"2209.02341","title":"EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models","created":"2022-09-06","cited_by_count":4,"area":null},{"id":"2308.06668","title":"Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges","created":"2023-08-13","cited_by_count":4,"area":null},{"id":"2504.09844","title":"MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training","created":"2025-04-14","cited_by_count":4,"area":null},{"id":"2604.21428","title":"Decoupled DiLoCo for Resilient Distributed Pre-training","created":"2026-04-23","cited_by_count":4,"area":null},{"id":"2205.09470","title":"Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2303.04338","title":"Provable Pathways: Learning Multiple Tasks over Multiple Paths","created":"2023-03-08","cited_by_count":3,"area":null},{"id":"2309.07181","title":"The Grand Illusion: The Myth of Software Portability and Implications for ML Progress","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2310.01154","title":"Modularity in Deep Learning: A Survey","created":"2023-10-02","cited_by_count":3,"area":null},{"id":"2310.07136","title":"Exponential Quantum Communication Advantage in Distributed Inference and Learning","created":"2023-10-11","cited_by_count":3,"area":null},{"id":"2403.17540","title":"Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction","created":"2024-03-26","cited_by_count":3,"area":null},{"id":"2410.10181","title":"Scalable Multi-Domain Adaptation of Language Models using Modular Experts","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2412.15411","title":"Sparse Checkpointing for Fast and Reliable MoE Training","created":"2024-12-19","cited_by_count":3,"area":"moe"},{"id":"2502.19790","title":"Mixtera: A Data Plane for Foundation Model Training","created":"2025-02-27","cited_by_count":3,"area":null},{"id":"2303.00501","title":"OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System","created":"2023-03-01","cited_by_count":2,"area":null},{"id":"2305.13235","title":"SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations","created":"2023-05-22","cited_by_count":2,"area":null},{"id":"2309.01226","title":"Saturn: An Optimized Data System for Large Model Deep Learning Workloads","created":"2023-09-03","cited_by_count":2,"area":null},{"id":"2401.01054","title":"Elastic Multi-Gradient Descent for Parallel Continual Learning","created":"2024-01-02","cited_by_count":2,"area":null},{"id":"2402.03460","title":"Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts","created":"2024-02-05","cited_by_count":2,"area":"moe"},{"id":"2510.27434","title":"Exploiting heterogeneous delays for efficient computation in low-bit neural networks","created":"2025-10-31","cited_by_count":2,"area":null},{"id":"2605.02162","title":"AAFLOW: Scalable Patterns for Agentic AI Workflows","created":"2026-05-04","cited_by_count":2,"area":"lr"},{"id":"2208.02148","title":"GPPF: A General Perception Pre-training Framework via Sparsely Activated Multi-Task Learning","created":"2022-08-03","cited_by_count":1,"area":null},{"id":"2302.02721","title":"Multipath agents for modular multitask ML systems","created":"2023-02-06","cited_by_count":1,"area":null},{"id":"2304.11987","title":"Causal fault localisation in dataflow systems","created":"2023-04-24","cited_by_count":1,"area":null},{"id":"2306.00088","title":"Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2606.25147","title":"TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2208.11069","title":"Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows","created":"2022-08-23","cited_by_count":0,"area":null},{"id":"2302.13741","title":"Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems","created":"2023-02-27","cited_by_count":0,"area":null},{"id":"2304.13540","title":"Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2312.12781","title":"DynaLay: An Introspective Approach to Dynamic Layer Selection for Deep Networks","created":"2023-12-20","cited_by_count":0,"area":null},{"id":"2501.01951","title":"MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators","created":"2025-01-03","cited_by_count":0,"area":null},{"id":"2605.15436","title":"Neural Activation Patterns Across Language Model Architectures: A Comprehensive Analysis of Cognitive Task Performance","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2606.11169","title":"Piper: A Programmable Distributed Training System","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2608.15762","title":"Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving","created":"2026-08-16","cited_by_count":0,"area":null}],"next_cursor":null}