{"total":85,"rows":[{"id":"2404.02948","title":"PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models","created":"2024-04-03","cited_by_count":269,"area":null},{"id":"2312.12148","title":"Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment","created":"2023-12-19","cited_by_count":230,"area":null},{"id":"2305.18703","title":"Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey","created":"2023-05-30","cited_by_count":135,"area":null},{"id":"2310.18339","title":"When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications","created":"2023-10-21","cited_by_count":111,"area":"moe"},{"id":"2302.11529","title":"Modular Deep Learning","created":"2023-02-22","cited_by_count":83,"area":null},{"id":"2302.03202","title":"Exploring the Benefits of Training Expert Language Models over Instruction Tuning","created":"2023-02-07","cited_by_count":80,"area":null},{"id":"2405.17604","title":"LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters","created":"2024-05-27","cited_by_count":76,"area":null},{"id":"2310.15080","title":"Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization","created":"2023-10-23","cited_by_count":65,"area":null},{"id":"2408.07057","title":"A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning","created":"2024-08-13","cited_by_count":52,"area":null},{"id":"2211.11720","title":"Multitask Vision-Language Prompt Tuning","created":"2022-11-21","cited_by_count":51,"area":"mllm"},{"id":"2406.11675","title":"BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models","created":"2024-06-17","cited_by_count":50,"area":null},{"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":"2401.08295","title":"SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models","created":"2024-01-16","cited_by_count":38,"area":null},{"id":"2402.15179","title":"Advancing Parameter Efficiency in Fine-tuning via Representation Editing","created":"2024-02-23","cited_by_count":37,"area":null},{"id":"2309.05173","title":"DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning","created":"2023-09-11","cited_by_count":35,"area":null},{"id":"2211.03831","title":"Multi-Head Adapter Routing for Cross-Task Generalization","created":"2022-11-07","cited_by_count":33,"area":null},{"id":"2208.10160","title":"PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation","created":"2022-08-22","cited_by_count":31,"area":"distill"},{"id":"2306.04933","title":"InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding","created":"2023-06-08","cited_by_count":30,"area":null},{"id":"2311.09344","title":"Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization","created":"2023-11-15","cited_by_count":28,"area":null},{"id":"2401.10545","title":"Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency","created":"2024-01-19","cited_by_count":28,"area":null},{"id":"2303.01580","title":"Mixture of Soft Prompts for Controllable Data Generation","created":"2023-03-02","cited_by_count":23,"area":null},{"id":"2305.01711","title":"Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner","created":"2023-05-02","cited_by_count":23,"area":null},{"id":"2405.13952","title":"Spectral Adapter: Fine-Tuning in Spectral Space","created":"2024-05-22","cited_by_count":21,"area":null},{"id":"2410.17875","title":"Understanding Layer Significance in LLM Alignment","created":"2024-10-23","cited_by_count":19,"area":"alignment"},{"id":"2310.10908","title":"Unlocking Emergent Modularity in Large Language Models","created":"2023-10-17","cited_by_count":17,"area":null},{"id":"2303.12314","title":"Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization","created":"2023-03-22","cited_by_count":15,"area":null},{"id":"2305.13406","title":"DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules","created":"2023-05-22","cited_by_count":13,"area":null},{"id":"2408.01119","title":"Task Prompt Vectors: Effective Initialization through Multi-Task Soft-Prompt Transfer","created":"2024-08-02","cited_by_count":13,"area":null},{"id":"2303.02909","title":"Dynamic Prompting: A Unified Framework for Prompt Tuning","created":"2023-03-06","cited_by_count":12,"area":null},{"id":"2312.02515","title":"mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs","created":"2023-12-05","cited_by_count":12,"area":null},{"id":"2504.04308","title":"Gating is Weighting: Understanding Gated Linear Attention through In-context Learning","created":"2025-04-06","cited_by_count":12,"area":null},{"id":"2305.13256","title":"TaskWeb: Selecting Better Source Tasks for Multi-task NLP","created":"2023-05-22","cited_by_count":11,"area":null},{"id":"2405.00657","title":"RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization","created":"2024-05-01","cited_by_count":11,"area":null},{"id":"2305.09025","title":"Soft Prompt Decoding for Multilingual Dense Retrieval","created":"2023-05-15","cited_by_count":10,"area":null},{"id":"2305.12576","title":"Automated Few-shot Classification with Instruction-Finetuned Language Models","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2505.22694","title":"MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning","created":"2025-05-28","cited_by_count":9,"area":null},{"id":"2310.10094","title":"Decomposed Prompt Tuning via Low-Rank Reparameterization","created":"2023-10-16","cited_by_count":8,"area":null},{"id":"2312.03248","title":"Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning","created":"2023-12-06","cited_by_count":8,"area":null},{"id":"2312.07255","title":"GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interaction","created":"2023-12-12","cited_by_count":8,"area":null},{"id":"2601.09684","title":"Disentangling Task Conflicts in Multi-Task LoRA via Orthogonal Gradient Projection","created":"2026-01-14","cited_by_count":8,"area":null},{"id":"2310.01691","title":"Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models","created":"2023-10-02","cited_by_count":7,"area":null},{"id":"2402.15082","title":"PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning","created":"2024-02-23","cited_by_count":7,"area":"moe"},{"id":"2507.06085","title":"A Survey on Prompt Tuning","created":"2025-07-08","cited_by_count":7,"area":null},{"id":"2310.11670","title":"Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning","created":"2023-10-18","cited_by_count":6,"area":null},{"id":"2410.00131","title":"Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models","created":"2024-09-30","cited_by_count":6,"area":null},{"id":"2412.11484","title":"Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents","created":"2024-12-16","cited_by_count":6,"area":"mllm"},{"id":"2503.18063","title":"Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning","created":"2025-03-23","cited_by_count":6,"area":null},{"id":"2503.20110","title":"Efficient Model Development through Fine-tuning Transfer","created":"2025-03-25","cited_by_count":6,"area":null},{"id":"2505.09519","title":"PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning","created":"2025-05-14","cited_by_count":6,"area":"moe"},{"id":"2407.02317","title":"Soft Language Prompts for Language Transfer","created":"2024-07-02","cited_by_count":5,"area":null},{"id":"2511.21285","title":"PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark","created":"2025-11-26","cited_by_count":5,"area":null},{"id":"2307.10685","title":"Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection","created":"2023-07-20","cited_by_count":4,"area":null},{"id":"2505.00009","title":"Efficient Knowledge Transfer in Multi-Task Learning through Task-Adaptive Low-Rank Representation","created":"2025-04-20","cited_by_count":4,"area":null},{"id":"2605.04970","title":"Skill Neologisms: Towards Skill-based Continual Learning","created":"2026-05-06","cited_by_count":4,"area":null},{"id":"2305.07019","title":"Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts","created":"2023-05-11","cited_by_count":3,"area":"mllm"},{"id":"2402.08594","title":"Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning","created":"2024-02-13","cited_by_count":3,"area":null},{"id":"2405.11464","title":"Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion","created":"2024-05-19","cited_by_count":3,"area":null},{"id":"2406.12251","title":"Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning","created":"2024-06-18","cited_by_count":3,"area":null},{"id":"2408.10567","title":"Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2411.17426","title":"CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2501.03291","title":"ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning","created":"2025-01-06","cited_by_count":3,"area":null},{"id":"2502.12200","title":"Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product","created":"2025-02-16","cited_by_count":3,"area":null},{"id":"2302.14413","title":"SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases","created":"2023-02-28","cited_by_count":2,"area":null},{"id":"2310.01217","title":"ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale","created":"2023-10-02","cited_by_count":2,"area":null},{"id":"2406.05279","title":"SuperPos-Prompt: Enhancing Soft Prompt Tuning of Language Models with Superposition of Multi Token Embeddings","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2406.19486","title":"LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models","created":"2024-06-27","cited_by_count":2,"area":null},{"id":"2407.16245","title":"Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning","created":"2024-07-23","cited_by_count":2,"area":null},{"id":"2408.13227","title":"Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition","created":"2024-08-23","cited_by_count":2,"area":null},{"id":"2410.09845","title":"Understanding Robustness of Parameter-Efficient Tuning for Image Classification","created":"2024-10-13","cited_by_count":1,"area":null},{"id":"2410.12847","title":"ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2511.10707","title":"Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning","created":"2025-11-13","cited_by_count":1,"area":"lr"},{"id":"2305.13499","title":"Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2310.02473","title":"Prompting-based Temporal Domain Generalization","created":"2023-10-03","cited_by_count":0,"area":null},{"id":"2310.18472","title":"Parameter-Efficient Methods for Metastases Detection from Clinical Notes","created":"2023-10-27","cited_by_count":0,"area":null},{"id":"2407.15556","title":"SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning","created":"2024-07-22","cited_by_count":0,"area":null},{"id":"2504.12311","title":"Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer","created":"2025-04-09","cited_by_count":0,"area":null},{"id":"2509.14253","title":"CrossPT: Exploring Cross-Task Transferability through Multi-Task Prompt Tuning","created":"2025-09-11","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":"2603.01135","title":"FCN-LLM: Empower LLM for Brain Functional Connectivity Network Understanding via Graph-level Multi-task Instruction Tuning","created":"2026-03-01","cited_by_count":0,"area":null},{"id":"2604.00533","title":"Toward Robust Open-set Adaptation: Synapse Consolidation Inspired by Rac1/MAPK Pathways","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.06650","title":"A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2607.27497","title":"SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge","created":"2026-07-29","cited_by_count":0,"area":"agents"},{"id":"2608.29790","title":"HiVe: Beyond Static Prompts for Multitask Learning via Hierarchy-based Vertical Mixture-of-Experts","created":"2026-08-30","cited_by_count":0,"area":"moe"},{"id":"2609.02639","title":"TaRA: Training-Aware Low-Rank Adaptation Initialization","created":"2026-09-02","cited_by_count":0,"area":null},{"id":"2610.00928","title":"Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}