{"total":97,"rows":[{"id":"2402.04333","title":"LESS: Selecting Influential Data for Targeted Instruction Tuning","created":"2024-02-06","cited_by_count":532,"area":null},{"id":"2305.17333","title":"Fine-Tuning Language Models with Just Forward Passes","created":"2023-05-27","cited_by_count":345,"area":null},{"id":"2303.14186","title":"TRAK: Attributing Model Behavior at Scale","created":"2023-03-24","cited_by_count":283,"area":null},{"id":"2305.12827","title":"Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models","created":"2023-05-22","cited_by_count":221,"area":null},{"id":"2303.15647","title":"Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning","created":"2023-03-28","cited_by_count":173,"area":null},{"id":"2305.19420","title":"What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization","created":"2023-05-30","cited_by_count":118,"area":null},{"id":"2311.12786","title":"Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks","created":"2023-11-21","cited_by_count":115,"area":null},{"id":"2302.06600","title":"Task-Specific Skill Localization in Fine-tuned Language Models","created":"2023-02-13","cited_by_count":105,"area":null},{"id":"2407.11046","title":"A Survey on LoRA of Large Language Models","created":"2024-07-08","cited_by_count":67,"area":null},{"id":"2310.08184","title":"Learning from models beyond fine-tuning","created":"2023-10-12","cited_by_count":48,"area":null},{"id":"2402.11867","title":"LoRA Training in the NTK Regime has No Spurious Local Minima","created":"2024-02-19","cited_by_count":48,"area":null},{"id":"2404.10859","title":"Forcing Diffuse Distributions out of Language Models","created":"2024-04-16","cited_by_count":48,"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":"2501.06425","title":"Tensor Product Attention Is All You Need","created":"2025-01-11","cited_by_count":31,"area":null},{"id":"2305.18787","title":"Universality and Limitations of Prompt Tuning","created":"2023-05-30","cited_by_count":30,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2406.02913","title":"Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity","created":"2024-06-05","cited_by_count":28,"area":null},{"id":"2303.02141","title":"Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!","created":"2023-03-03","cited_by_count":27,"area":null},{"id":"2312.15184","title":"ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization","created":"2023-12-23","cited_by_count":25,"area":null},{"id":"2308.10502","title":"GradientCoin: A Peer-to-Peer Decentralized Large Language Models","created":"2023-08-21","cited_by_count":23,"area":null},{"id":"2405.16747","title":"Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective","created":"2024-05-27","cited_by_count":22,"area":null},{"id":"2405.16391","title":"When does compositional structure yield compositional generalization? A kernel theory","created":"2024-05-26","cited_by_count":20,"area":null},{"id":"2409.06091","title":"Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity","created":"2024-09-09","cited_by_count":20,"area":null},{"id":"2502.09376","title":"LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail)","created":"2025-02-13","cited_by_count":20,"area":null},{"id":"2307.15196","title":"The Marginal Value of Momentum for Small Learning Rate SGD","created":"2023-07-27","cited_by_count":18,"area":null},{"id":"2405.02596","title":"Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning","created":"2024-05-04","cited_by_count":17,"area":null},{"id":"2212.14852","title":"An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models","created":"2022-12-30","cited_by_count":16,"area":null},{"id":"2504.02620","title":"Efficient Model Editing with Task-Localized Sparse Fine-tuning","created":"2025-04-03","cited_by_count":16,"area":null},{"id":"2504.14662","title":"Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning","created":"2025-04-20","cited_by_count":16,"area":null},{"id":"2303.01687","title":"Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation","created":"2023-03-03","cited_by_count":15,"area":null},{"id":"2302.01428","title":"Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation","created":"2023-02-02","cited_by_count":14,"area":null},{"id":"2406.04606","title":"Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions","created":"2024-06-07","cited_by_count":14,"area":null},{"id":"2407.13621","title":"Differential Privacy Mechanisms in Neural Tangent Kernel Regression","created":"2024-07-18","cited_by_count":14,"area":null},{"id":"2607.21716","title":"A Defense of the Quadratic Model","created":"2026-07-23","cited_by_count":14,"area":null},{"id":"2409.19458","title":"Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach","created":"2024-09-28","cited_by_count":13,"area":null},{"id":"2503.06982","title":"Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization","created":"2025-03-10","cited_by_count":13,"area":null},{"id":"2310.12462","title":"Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights","created":"2023-10-19","cited_by_count":12,"area":null},{"id":"2502.05075","title":"Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension","created":"2025-02-07","cited_by_count":12,"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":"2512.23017","title":"Merge before Forget: A Single LoRA Continual Learning via Continual Merging","created":"2025-12-28","cited_by_count":11,"area":null},{"id":"2310.18526","title":"Sample based Explanations via Generalized Representers","created":"2023-10-27","cited_by_count":10,"area":null},{"id":"2502.00620","title":"Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions","created":"2025-02-02","cited_by_count":10,"area":null},{"id":"2503.09493","title":"Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection","created":"2025-03-12","cited_by_count":10,"area":null},{"id":"2512.18248","title":"On the Convergence Rate of LoRA Gradient Descent","created":"2025-12-20","cited_by_count":10,"area":null},{"id":"2310.13225","title":"Scalable Neural Network Kernels","created":"2023-10-20","cited_by_count":9,"area":null},{"id":"2405.15485","title":"Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs","created":"2024-05-24","cited_by_count":9,"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":"2505.18346","title":"On the Mechanisms of Weak-to-Strong Generalization: A Theoretical Perspective","created":"2025-05-23","cited_by_count":8,"area":null},{"id":"2210.17011","title":"A picture of the space of typical learnable tasks","created":"2022-10-31","cited_by_count":7,"area":null},{"id":"2507.06558","title":"The Primacy of Magnitude in Low-Rank Adaptation","created":"2025-07-09","cited_by_count":7,"area":null},{"id":"2606.29554","title":"Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise","created":"2026-06-28","cited_by_count":7,"area":null},{"id":"2403.13893","title":"DAVED: Data Acquisition via Experimental Design for Data Markets","created":"2024-03-20","cited_by_count":6,"area":null},{"id":"2505.13430","title":"Fine-tuning Quantized Neural Networks with Zeroth-order Optimization","created":"2025-05-19","cited_by_count":6,"area":null},{"id":"2411.05873","title":"Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach","created":"2024-11-07","cited_by_count":5,"area":null},{"id":"2411.15385","title":"Gradient dynamics for low-rank fine-tuning beyond kernels","created":"2024-11-23","cited_by_count":5,"area":null},{"id":"2602.20062","title":"A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning","created":"2026-02-23","cited_by_count":5,"area":null},{"id":"2507.07694","title":"SAS: Simulated Attention Score","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2511.07380","title":"Mining Useful General Data for Low-Resource Domain Adaptation","created":"2025-11-10","cited_by_count":4,"area":null},{"id":"2505.18494","title":"FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2508.00180","title":"EMA Without the Lag: Bias-Corrected Iterate Averaging Schemes","created":"2025-07-31","cited_by_count":3,"area":null},{"id":"2509.23592","title":"Toward a Holistic Approach to Continual Model Merging","created":"2025-09-28","cited_by_count":3,"area":null},{"id":"2602.17385","title":"Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature","created":"2026-02-19","cited_by_count":3,"area":null},{"id":"2605.31484","title":"Balanced LoRA: Removing Parameter Invariance to Accelerate Convergence","created":"2026-05-29","cited_by_count":3,"area":null},{"id":"2402.15441","title":"Active Few-Shot Fine-Tuning","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2508.01348","title":"Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning","created":"2025-08-02","cited_by_count":2,"area":null},{"id":"2509.24005","title":"Does Weak-to-strong Generalization Happen under Spurious Correlations?","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2512.22088","title":"Unifying Learning Dynamics and Generalization in Transformers Scaling Law","created":"2025-12-26","cited_by_count":2,"area":null},{"id":"2601.22563","title":"EUGens: Efficient, Unified, and General Dense Layers","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2602.08239","title":"Linearization Explains Fine-Tuning in Large Language Models","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2602.13071","title":"Bus-Conditioned Zero-Shot Trajectory Generation via Task Arithmetic","created":"2026-02-13","cited_by_count":2,"area":null},{"id":"2310.16261","title":"The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2403.17467","title":"A Unified Kernel for Neural Network Learning","created":"2024-03-26","cited_by_count":1,"area":null},{"id":"2505.16260","title":"Small-to-Large Generalization: Data Influences Models Consistently Across Scale","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2602.13942","title":"A Theoretical Framework for LLM Fine-tuning Using Early Stopping for Non-random Initialization","created":"2026-02-15","cited_by_count":1,"area":null},{"id":"2603.29824","title":"Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2605.03373","title":"Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2605.03724","title":"Rethinking the Rank Threshold for LoRA Fine-Tuning","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2605.27774","title":"Fine-Tuning Dynamics of In-Context Factual Recall in Transformers","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2403.08484","title":"Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2406.00509","title":"Empirical influence functions to understand the logic of fine-tuning","created":"2024-06-01","cited_by_count":0,"area":null},{"id":"2408.00359","title":"Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks","created":"2024-08-01","cited_by_count":0,"area":null},{"id":"2410.00699","title":"Investigating the Impact of Model Complexity in Large Language Models","created":"2024-10-01","cited_by_count":0,"area":null},{"id":"2410.09771","title":"EUGens: Efficient, Unified, and General Dense Layers","created":"2024-10-13","cited_by_count":0,"area":null},{"id":"2507.01841","title":"Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization","created":"2025-07-02","cited_by_count":0,"area":null},{"id":"2507.04779","title":"Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2604.08368","title":"SOLAR: Communication-Efficient Model Adaptation via Subspace-Oriented Latent Adapter Reparametrization","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2605.10741","title":"AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.11872","title":"LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.22972","title":"A mathematical theory of balancing relational generalization and memorization","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.12883","title":"The Hidden Power of Scaling Factor in LoRA Optimization","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2609.32271","title":"Certification Frontiers for Gaussian LoRA: Independent Priors, Posterior Risk, and Prediction-Preserving Balancing","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.32818","title":"When Can First-Order Models of Fine-Tuning Bound Forgetting?","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33620","title":"Learning Dynamics of Continual Learning: A Unified View of Data Attribution, Forgetting, and Plasticity Loss","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34667","title":"Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.34756","title":"Statistical Benefits of Fine-Tuning from Pretrained Initialization in Diagonal Linear Networks","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.13568","title":"Rationally Linearized MLPs","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.17686","title":"Encoding Context into LLM Weights: What Makes It Context?","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}