Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning | Paper Citation Record

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A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 156

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Entry-level guide to the use of large language models for medical research cites this paper.

Entry-level guide to the use of large language models for medical research Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 12

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An Empirical Study of Vulnerability Detection using Federated Learning cites this paper.

An Empirical Study of Vulnerability Detection using Federated Learning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 43

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Parameter Efficient Instruction Tuning: An Empirical Study cites this paper.

Parameter Efficient Instruction Tuning: An Empirical Study Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 12

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A Primer on Large Language Models and their Limitations cites this paper.

A Primer on Large Language Models and their Limitations Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 38

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KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models cites this paper.

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 31

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BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation cites this paper.

BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 18

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TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain cites this paper.

TelcoLM: collecting data, adapting, and benchmarking language models for the telecommunication domain Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 21

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GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection cites this paper.

GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 26

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A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation cites this paper.

A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 37

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Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding cites this paper.

Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 15

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Foundations of Large Language Models cites this paper.

Foundations of Large Language Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 148

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A Resource-Efficient Training Framework for Remote Sensing Text--Image Retrieval cites this paper.

A Resource-Efficient Training Framework for Remote Sensing Text--Image Retrieval Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 2023

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EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition cites this paper.

EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 33

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale cites this paper.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 41

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TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems cites this paper.

TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 46

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Federated Client-tailored Adapter for Medical Image Segmentation cites this paper.

Federated Client-tailored Adapter for Medical Image Segmentation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 44

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A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models cites this paper.

A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 76

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Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes cites this paper.

Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 34

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Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning cites this paper.

Look Within or Look Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 35

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Large Language Models for Detection of Life-Threatening Texts cites this paper.

Large Language Models for Detection of Life-Threatening Texts Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 9

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15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning cites this paper.

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 49

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LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning cites this paper.

LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 25

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Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 132

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Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 21

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Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models cites this paper.

Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 18

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CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation cites this paper.

CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 30

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Time Series Foundation Models for Multivariate Financial Time Series Forecasting cites this paper.

Time Series Foundation Models for Multivariate Financial Time Series Forecasting Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 69

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Enhancing RLHF with Human Gaze Modeling cites this paper.

Enhancing RLHF with Human Gaze Modeling Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 19

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AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air cites this paper.

AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 4

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Parameter-Efficient Fine-Tuning of Foundation Models for CLP Speech Classification cites this paper.

Parameter-Efficient Fine-Tuning of Foundation Models for CLP Speech Classification Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 18

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HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging cites this paper.

HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 30

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CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams cites this paper.

CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 2015

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CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values cites this paper.

CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 45

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Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs cites this paper.

Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 44

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On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization cites this paper.

On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 5

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PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark cites this paper.

PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 32

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PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models cites this paper.

PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 42

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Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches cites this paper.

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 6

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Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches cites this paper.

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 6

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One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 41

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One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization cites this paper.

One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 40

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CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation cites this paper.

CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 35

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Observation eea2ccce-4369-44d8-8af1-1f1ecf39525e · inbound

Are Large Language Models Economically Viable for Industry Deployment? cites this paper.

Are Large Language Models Economically Viable for Industry Deployment? Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 43

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MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks cites this paper.

MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 31

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Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks cites this paper.

Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 30

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Observation fd22335a-bfcf-4577-9e20-5ff1fad93643 · inbound

Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution cites this paper.

Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:11:25.783765Z

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Observation 5ede5335-d156-4e02-af0e-8ffcb4856eee · inbound

Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution cites this paper.

Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:55:46.479266Z

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Observation 43baa535-edba-4ff4-a51a-0765001f6666 · inbound

Combining pre-trained models via localized model averaging cites this paper.

Combining pre-trained models via localized model averaging Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 151

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Observation 6fae0f60-f413-41a8-b584-c66737e1ccc7 · inbound

Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning cites this paper.

Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-29T10:03:17.284235Z

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Observation fad8b116-36fe-4403-a8bf-2f4eac67dd8e · inbound

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services cites this paper.

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:34:48.826605Z

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Observation 0f4cecbd-f25e-4a49-bd7a-2056e6b79bd2 · inbound

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs cites this paper.

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 94

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Observation c8123b33-4c6b-4407-941a-4eed5330fb7a · inbound

Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning cites this paper.

Beyond Rotations: AuroOFT for Expressive Quantized Orthogonal Fine-Tuning Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T17:05:01.897883Z

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Observation b1df4501-1674-4086-84be-0c3ad29511a4 · inbound

V-FiLLM: Verified Financial LLM Reasoning Benchmark cites this paper.

V-FiLLM: Verified Financial LLM Reasoning Benchmark Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:14.533197Z

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