{"total":69,"rows":[{"id":"2303.18223","title":"A Survey of Large Language Models","created":"2023-03-31","cited_by_count":2576,"area":null},{"id":"2403.13372","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","created":"2024-03-20","cited_by_count":1464,"area":null},{"id":"2307.06435","title":"A Comprehensive Overview of Large Language Models","created":"2023-07-12","cited_by_count":648,"area":null},{"id":"2305.16264","title":"Scaling Data-Constrained Language Models","created":"2023-05-25","cited_by_count":403,"area":null},{"id":"2503.09642","title":"Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k","created":"2025-03-12","cited_by_count":163,"area":null},{"id":"2405.19888","title":"Parrot: Efficient Serving of LLM-based Applications with Semantic Variable","created":"2024-05-30","cited_by_count":99,"area":null},{"id":"2310.18313","title":"FP8-LM: Training FP8 Large Language Models","created":"2023-10-27","cited_by_count":77,"area":null},{"id":"2311.12351","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey","created":"2023-11-21","cited_by_count":72,"area":null},{"id":"2402.09748","title":"Model Compression and Efficient Inference for Large Language Models: A Survey","created":"2024-02-15","cited_by_count":64,"area":null},{"id":"2405.07719","title":"USP: A Unified Sequence Parallelism Approach for Long Context Generative AI","created":"2024-05-13","cited_by_count":61,"area":null},{"id":"2401.02038","title":"Understanding LLMs: A Comprehensive Overview from Training to Inference","created":"2024-01-04","cited_by_count":59,"area":null},{"id":"2402.05964","title":"A Survey on Transformer Compression","created":"2024-02-05","cited_by_count":46,"area":null},{"id":"2501.16975","title":"Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling","created":"2025-01-28","cited_by_count":44,"area":null},{"id":"2302.01107","title":"A Survey on Efficient Training of Transformers","created":"2023-02-02","cited_by_count":39,"area":null},{"id":"2304.03589","title":"On Efficient Training of Large-Scale Deep Learning Models: A Literature Review","created":"2023-04-07","cited_by_count":26,"area":null},{"id":"2401.06532","title":"INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning","created":"2024-01-12","cited_by_count":20,"area":null},{"id":"2306.13945","title":"Large Sequence Models for Sequential Decision-Making: A Survey","created":"2023-06-24","cited_by_count":19,"area":null},{"id":"2211.16742","title":"Protein Language Models and Structure Prediction: Connection and Progression","created":"2022-11-30","cited_by_count":17,"area":null},{"id":"2409.17892","title":"EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models","created":"2024-09-26","cited_by_count":16,"area":null},{"id":"2304.08480","title":"DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training","created":"2023-04-17","cited_by_count":15,"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":"2305.19370","title":"Blockwise Parallel Transformer for Large Context Models","created":"2023-05-30","cited_by_count":12,"area":null},{"id":"2404.10933","title":"LLMem: Estimating GPU Memory Usage for Fine-Tuning Pre-Trained LLMs","created":"2024-04-16","cited_by_count":12,"area":null},{"id":"2206.04959","title":"Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models","created":"2022-06-10","cited_by_count":11,"area":null},{"id":"2310.00576","title":"GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length","created":"2023-10-01","cited_by_count":11,"area":null},{"id":"2505.01043","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2404.00399","title":"Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code","created":"2024-03-30","cited_by_count":10,"area":null},{"id":"2310.14819","title":"Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic","created":"2023-10-23","cited_by_count":7,"area":null},{"id":"2409.01990","title":"Designing Large Foundation Models for Efficient Training and Inference: A Survey","created":"2024-09-03","cited_by_count":7,"area":null},{"id":"2502.08145","title":"Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers","created":"2025-02-12","cited_by_count":7,"area":null},{"id":"2312.14647","title":"Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities","created":"2023-12-22","cited_by_count":5,"area":null},{"id":"2402.10618","title":"Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement","created":"2024-02-16","cited_by_count":5,"area":null},{"id":"2509.05276","title":"SpikingBrain: Spiking Brain-inspired Large Models","created":"2025-09-05","cited_by_count":5,"area":null},{"id":"2607.09084","title":"A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design","created":"2026-07-10","cited_by_count":5,"area":null},{"id":"2208.05321","title":"A Frequency-aware Software Cache for Large Recommendation System Embeddings","created":"2022-08-08","cited_by_count":4,"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":"2308.14149","title":"Examining User-Friendly and Open-Sourced Large GPT Models: A Survey on Language, Multimodal, and Scientific GPT Models","created":"2023-08-27","cited_by_count":4,"area":null},{"id":"2406.08334","title":"ProTrain: Efficient LLM Training via Memory-Aware Techniques","created":"2024-06-12","cited_by_count":4,"area":null},{"id":"2409.04833","title":"Achieving Peak Performance for Large Language Models: A Systematic Review","created":"2024-09-07","cited_by_count":4,"area":null},{"id":"2312.00407","title":"CoLLiE: Collaborative Training of Large Language Models in an Efficient Way","created":"2023-12-01","cited_by_count":3,"area":null},{"id":"2403.04790","title":"Online Training of Large Language Models: Learn while chatting","created":"2024-03-04","cited_by_count":3,"area":null},{"id":"2501.11847","title":"A Survey on Memory-Efficient Transformer-Based Model Training in AI for Science","created":"2025-01-21","cited_by_count":3,"area":null},{"id":"2501.09171","title":"Generative AI Takes a Statistics Exam: A Comparison of Performance between ChatGPT3.5, ChatGPT4, and ChatGPT4o-mini","created":"2025-01-15","cited_by_count":2,"area":null},{"id":"2509.03047","title":"FlashRecovery: Fast and Low-Cost Recovery from Failures for Large-Scale Training of LLMs","created":"2025-09-03","cited_by_count":2,"area":null},{"id":"2604.17172","title":"UCCL-Zip: Lossless Compression Supercharged GPU Communication","created":"2026-04-19","cited_by_count":2,"area":null},{"id":"2207.11912","title":"Dive into Big Model Training","created":"2022-07-25","cited_by_count":1,"area":null},{"id":"2312.04025","title":"Moirai: Towards Optimal Placement for Distributed Inference on Heterogeneous Devices","created":"2023-12-07","cited_by_count":1,"area":null},{"id":"2410.18701","title":"BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching","created":"2024-10-24","cited_by_count":1,"area":null},{"id":"2412.12636","title":"TrainMover: An Interruption-Resilient Runtime for ML Training","created":"2024-12-17","cited_by_count":1,"area":null},{"id":"2505.04083","title":"Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2506.12204","title":"Semantic Scheduling for LLM Inference","created":"2025-06-13","cited_by_count":1,"area":null},{"id":"2509.07003","title":"veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD","created":"2025-09-05","cited_by_count":1,"area":null},{"id":"2509.12266","title":"Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models","created":"2025-09-13","cited_by_count":1,"area":null},{"id":"2510.03049","title":"When and Where do Events Switch in Multi-Event Video Generation?","created":"2025-10-03","cited_by_count":1,"area":null},{"id":"2306.13835","title":"Computron: Serving Distributed Deep Learning Models with Model Parallel Swapping","created":"2023-06-24","cited_by_count":0,"area":null},{"id":"2309.08272","title":"Structural Self-Supervised Objectives for Transformers","created":"2023-09-15","cited_by_count":0,"area":null},{"id":"2402.09589","title":"MLCC: A Congestion Control Technique to Accelerate ML Training","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2405.18047","title":"2BP: 2-Stage Backpropagation","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2410.00273","title":"Comprehensive Performance Modeling and System Design Insights for Foundation Models","created":"2024-09-30","cited_by_count":0,"area":null},{"id":"2410.15155","title":"On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training","created":"2024-10-19","cited_by_count":0,"area":null},{"id":"2509.02480","title":"MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2509.26246","title":"SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2510.14614","title":"First Attentions Last: Better Exploiting First Attentions for Efficient Transformer Training","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2512.02764","title":"PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models","created":"2025-12-02","cited_by_count":0,"area":null},{"id":"2602.00014","title":"What Artificial Intelligence can do for High-Performance Computing systems?","created":"2026-01-03","cited_by_count":0,"area":null},{"id":"2603.16428","title":"An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2604.02651","title":"Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2605.30728","title":"Reducing the GPU Memory Bottleneck with Lossless Compression for ML -- Extended","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.30460","title":"HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models","created":"2026-06-29","cited_by_count":0,"area":null}],"next_cursor":null}