{"total":357,"rows":[{"id":"2403.07815","title":"Chronos: Learning the Language of Time Series","created":"2024-03-12","cited_by_count":759,"area":null},{"id":"2312.03863","title":"Efficient Large Language Models: A Survey","created":"2023-12-06","cited_by_count":186,"area":null},{"id":"2404.15159","title":"MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts","created":"2024-04-22","cited_by_count":147,"area":"moe"},{"id":"2308.03303","title":"LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning","created":"2023-08-07","cited_by_count":137,"area":null},{"id":"2402.16842","title":"Asymmetry in Low-Rank Adapters of Foundation Models","created":"2024-02-26","cited_by_count":86,"area":null},{"id":"2403.05231","title":"Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance","created":"2024-03-08","cited_by_count":70,"area":null},{"id":"2409.14924","title":"Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely","created":"2024-09-23","cited_by_count":66,"area":"rag"},{"id":"2402.12851","title":"MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models","created":"2024-02-20","cited_by_count":56,"area":"moe"},{"id":"2410.19878","title":"Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies","created":"2024-10-24","cited_by_count":47,"area":null},{"id":"2410.01623","title":"Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?","created":"2024-10-02","cited_by_count":43,"area":null},{"id":"2402.18153","title":"Diffusion-Based Neural Network Weights Generation","created":"2024-02-28","cited_by_count":39,"area":null},{"id":"2410.16103","title":"LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics","created":"2024-10-21","cited_by_count":38,"area":null},{"id":"2501.00365","title":"Low-Rank Adaptation for Foundation Models: A Comprehensive Review","created":"2024-12-31","cited_by_count":37,"area":null},{"id":"2502.00987","title":"RandLoRA: Full-rank parameter-efficient fine-tuning of large models","created":"2025-02-03","cited_by_count":35,"area":null},{"id":"2510.08396","title":"FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts","created":"2025-10-09","cited_by_count":35,"area":"moe"},{"id":"2405.10313","title":"How Far Are We From AGI: Are LLMs All We Need?","created":"2024-05-16","cited_by_count":31,"area":null},{"id":"2509.17786","title":"Accurate and Efficient Low-Rank Model Merging in Core Space","created":"2025-09-22","cited_by_count":29,"area":null},{"id":"2505.24816","title":"CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning","created":"2025-05-30","cited_by_count":28,"area":null},{"id":"2408.04638","title":"Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective","created":"2024-07-30","cited_by_count":25,"area":null},{"id":"2410.06040","title":"QERA: an Analytical Framework for Quantization Error Reconstruction","created":"2024-10-08","cited_by_count":22,"area":null},{"id":"2411.19557","title":"Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning","created":"2024-11-29","cited_by_count":22,"area":null},{"id":"2512.16301","title":"Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills","created":"2025-12-18","cited_by_count":22,"area":"agents"},{"id":"2505.20809","title":"Improved Representation Steering for Language Models","created":"2025-05-27","cited_by_count":21,"area":"mechinterp"},{"id":"2502.11812","title":"Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis","created":"2025-02-17","cited_by_count":20,"area":null},{"id":"2410.17875","title":"Understanding Layer Significance in LLM Alignment","created":"2024-10-23","cited_by_count":19,"area":"alignment"},{"id":"2411.10928","title":"Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning","created":"2024-11-17","cited_by_count":19,"area":"mllm"},{"id":"2504.07448","title":"LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation","created":"2025-04-10","cited_by_count":19,"area":null},{"id":"2410.09432","title":"FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models","created":"2024-10-12","cited_by_count":18,"area":null},{"id":"2507.00029","title":"LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing","created":"2025-06-17","cited_by_count":18,"area":"moe"},{"id":"2410.09437","title":"MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning","created":"2024-10-12","cited_by_count":17,"area":null},{"id":"2410.17881","title":"AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning","created":"2024-10-23","cited_by_count":17,"area":null},{"id":"2504.21099","title":"A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning","created":"2025-04-29","cited_by_count":17,"area":null},{"id":"2510.08439","title":"xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning","created":"2025-10-09","cited_by_count":17,"area":null},{"id":"2507.19672","title":"Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges","created":"2025-07-25","cited_by_count":15,"area":"alignment"},{"id":"2410.10360","title":"Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning","created":"2024-10-14","cited_by_count":14,"area":"rag"},{"id":"2506.20629","title":"PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models","created":"2025-06-25","cited_by_count":14,"area":null},{"id":"2602.04118","title":"Learning to Reason in 13 Parameters","created":"2026-02-04","cited_by_count":14,"area":"lr"},{"id":"2501.15103","title":"Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning","created":"2025-01-25","cited_by_count":13,"area":"moe"},{"id":"2509.25684","title":"LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts","created":"2025-09-30","cited_by_count":13,"area":"moe"},{"id":"2410.15483","title":"Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspective","created":"2024-10-20","cited_by_count":12,"area":"alignment"},{"id":"2502.12171","title":"GoRA: Gradient-driven Adaptive Low Rank Adaptation","created":"2025-02-13","cited_by_count":12,"area":null},{"id":"2505.12805","title":"FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA","created":"2025-05-19","cited_by_count":11,"area":null},{"id":"2506.13771","title":"LittleBit: Ultra Low-Bit Quantization via Latent Factorization","created":"2025-05-30","cited_by_count":11,"area":null},{"id":"2506.14530","title":"Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters","created":"2025-06-17","cited_by_count":11,"area":null},{"id":"2408.09856","title":"TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition","created":"2024-08-19","cited_by_count":10,"area":null},{"id":"2508.00230","title":"Towards Higher Effective Rank in Parameter-efficient Fine-tuning using Khatri--Rao Product","created":"2025-08-01","cited_by_count":10,"area":null},{"id":"2602.04998","title":"Learning Rate Matters: Vanilla LoRA May Suffice for LLM Fine-tuning","created":"2026-02-04","cited_by_count":10,"area":null},{"id":"2410.09489","title":"Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks","created":"2024-10-12","cited_by_count":9,"area":"lr"},{"id":"2504.09223","title":"DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models","created":"2025-04-12","cited_by_count":9,"area":null},{"id":"2505.12455","title":"AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections","created":"2025-05-18","cited_by_count":9,"area":null},{"id":"2505.13502","title":"Federated Low-Rank Adaptation for Foundation Models: A Survey","created":"2025-05-16","cited_by_count":9,"area":null},{"id":"2509.16415","title":"StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes","created":"2025-09-19","cited_by_count":9,"area":null},{"id":"2510.24966","title":"Sequences of Logits Reveal the Low Rank Structure of Language Models","created":"2025-10-28","cited_by_count":9,"area":null},{"id":"2401.16184","title":"Vocabulary-Defined Semantics: Latent Space Clustering for Improving In-Context Learning","created":"2024-01-29","cited_by_count":8,"area":null},{"id":"2410.01870","title":"PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers","created":"2024-10-02","cited_by_count":8,"area":null},{"id":"2410.10075","title":"RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2410.10880","title":"Fine-tuning can Help Detect Pretraining Data from Large Language Models","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2412.00071","title":"COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection","created":"2024-11-26","cited_by_count":8,"area":null},{"id":"2412.09250","title":"GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning","created":"2024-12-12","cited_by_count":8,"area":null},{"id":"2509.25622","title":"Layer-wise dynamic rank for compressing large language models","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2408.04556","title":"BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models","created":"2024-08-08","cited_by_count":7,"area":null},{"id":"2410.03941","title":"AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models","created":"2024-10-04","cited_by_count":7,"area":"diffusion"},{"id":"2503.21694","title":"Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data","created":"2025-03-27","cited_by_count":7,"area":"diffusion"},{"id":"2509.18993","title":"CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure","created":"2025-09-23","cited_by_count":7,"area":null},{"id":"2511.17979","title":"FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning","created":"2025-11-22","cited_by_count":7,"area":"diffusion"},{"id":"2403.10971","title":"Task-Aware Low-Rank Adaptation of Segment Anything Model","created":"2024-03-16","cited_by_count":6,"area":null},{"id":"2408.09397","title":"Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony","created":"2024-08-18","cited_by_count":6,"area":null},{"id":"2410.13618","title":"LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-Tuning","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2411.09289","title":"StreamAdapter: Efficient Test Time Adaptation from Contextual Streams","created":"2024-11-14","cited_by_count":6,"area":null},{"id":"2501.15377","title":"Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation","created":"2025-01-26","cited_by_count":6,"area":null},{"id":"2505.08022","title":"Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks","created":"2025-05-12","cited_by_count":6,"area":null},{"id":"2510.15962","title":"CTR-LoRA: Curvature-Aware and Trust-Region Guided Low-Rank Adaptation for Large Language Models","created":"2025-10-11","cited_by_count":6,"area":null},{"id":"2601.02273","title":"TopoLoRA-SAM: Topology-Aware Parameter-Efficient Adaptation of Foundation Segmenters for Thin-Structure and Cross-Domain Binary Semantic Segmentation","created":"2026-01-05","cited_by_count":6,"area":null},{"id":"2403.19456","title":"Break-for-Make: Modular Low-Rank Adaptations for Composable Content-Style Customization","created":"2024-03-28","cited_by_count":5,"area":null},{"id":"2405.09394","title":"SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning","created":"2024-05-15","cited_by_count":5,"area":null},{"id":"2407.02317","title":"Soft Language Prompts for Language Transfer","created":"2024-07-02","cited_by_count":5,"area":null},{"id":"2409.00055","title":"SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models","created":"2024-08-21","cited_by_count":5,"area":null},{"id":"2504.00254","title":"ElaLoRA: Elastic & Learnable Low-Rank Adaptation for Efficient Model Fine-Tuning","created":"2025-03-31","cited_by_count":5,"area":null},{"id":"2505.03756","title":"Improving the Serving Performance of Multi-LoRA Large Language Models via Efficient LoRA and KV Cache Management","created":"2025-04-19","cited_by_count":5,"area":"inference"},{"id":"2505.22922","title":"Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking","created":"2025-05-28","cited_by_count":5,"area":null},{"id":"2506.17475","title":"A geometric framework for momentum-based optimizers for low-rank training","created":"2025-06-20","cited_by_count":5,"area":null},{"id":"2506.21035","title":"Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts","created":"2025-06-26","cited_by_count":5,"area":"moe"},{"id":"2509.17874","title":"Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models","created":"2025-09-22","cited_by_count":5,"area":null},{"id":"2510.15416","title":"Adaptive Minds: Empowering Agents with LoRA-as-Tools","created":"2025-10-17","cited_by_count":5,"area":"agents"},{"id":"2601.22905","title":"FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation","created":"2026-01-30","cited_by_count":5,"area":null},{"id":"2602.17095","title":"FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment","created":"2026-02-19","cited_by_count":5,"area":null},{"id":"2603.05235","title":"Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning","created":"2026-03-05","cited_by_count":5,"area":null},{"id":"2603.17946","title":"CARE: Covariance-Aware and Rank-Enhanced Decomposition for Enabling Multi-Head Latent Attention","created":"2026-03-18","cited_by_count":5,"area":"inference"},{"id":"2405.07913","title":"CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models","created":"2024-05-13","cited_by_count":4,"area":null},{"id":"2410.14971","title":"BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation","created":"2024-10-19","cited_by_count":4,"area":null},{"id":"2410.16088","title":"Fine-Tuning LLMs for Reliable Medical Question-Answering Services","created":"2024-10-21","cited_by_count":4,"area":null},{"id":"2502.03029","title":"On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2502.06820","title":"LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2503.00572","title":"LoR2C : Low-Rank Residual Connection Adaptation for Parameter-Efficient Fine-Tuning","created":"2025-03-01","cited_by_count":4,"area":null},{"id":"2503.19201","title":"A Shared Low-Rank Adaptation Approach to Personalized RLHF","created":"2025-03-24","cited_by_count":4,"area":"alignment"},{"id":"2503.23360","title":"Not All LoRA Parameters Are Essential: Insights on Inference Necessity","created":"2025-03-30","cited_by_count":4,"area":null},{"id":"2504.07061","title":"Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer","created":"2025-04-09","cited_by_count":4,"area":null},{"id":"2506.02724","title":"WeightLoRA: Keep Only Necessary Adapters","created":"2025-06-03","cited_by_count":4,"area":null},{"id":"2506.09105","title":"MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning","created":"2025-06-10","cited_by_count":4,"area":null},{"id":"2506.14796","title":"PFMBench: Protein Foundation Model Benchmark","created":"2025-06-01","cited_by_count":4,"area":null}],"next_cursor":"100"}