Copertina

PAFT: Prompt-Agnostic Fine-Tuning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wei, Chenxing, Shu, Yao, Ou, Mingwen, He, Ying Tiffany, Yu, Fei Richard
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Flexora: Flexible Low Rank Adaptation for Large Language Models
di: Wei, Chenxing, et al.
Pubblicazione: (2024)

ReDit: Reward Dithering for Improved LLM Policy Optimization
di: Wei, Chenxing, et al.
Pubblicazione: (2025)

Prior Prompt Engineering for Reinforcement Fine-Tuning
di: Taveekitworachai, Pittawat, et al.
Pubblicazione: (2025)

Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning
di: Shernazarov, Ulugbek, et al.
Pubblicazione: (2026)

TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning
di: Ma, Dabiao, et al.
Pubblicazione: (2025)

PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning
di: Pentyala, Shiva Kumar, et al.
Pubblicazione: (2024)

FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation
di: Li, Bangzheng, et al.
Pubblicazione: (2024)

Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
di: Song, Feifan, et al.
Pubblicazione: (2024)

State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?
di: Pungeršek, Taja Kuzman, et al.
Pubblicazione: (2025)

Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
di: Cruickshank, Iain J., et al.
Pubblicazione: (2023)

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
di: Ding, Fei, et al.
Pubblicazione: (2025)

ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
di: Lin, Yu-Chen, et al.
Pubblicazione: (2024)

Instruction Fine-Tuning: Does Prompt Loss Matter?
di: Huerta-Enochian, Mathew, et al.
Pubblicazione: (2024)

Using LLMs for Automated Privacy Policy Analysis: Prompt Engineering, Fine-Tuning and Explainability
di: Chen, Yuxin, et al.
Pubblicazione: (2025)

Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning
di: Li, Ang, et al.
Pubblicazione: (2025)

PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
di: Lin, Ying-Jia, et al.
Pubblicazione: (2026)

Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation
di: Yu, Yinglong, et al.
Pubblicazione: (2025)

Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
di: Wang, Luping, et al.
Pubblicazione: (2024)

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity
di: Martin, Geoffrey, et al.
Pubblicazione: (2026)

Enhancing Multimodal Continual Instruction Tuning with BranchLoRA
di: Zhang, Duzhen, et al.
Pubblicazione: (2025)

ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims
di: Anik, Anirban Saha, et al.
Pubblicazione: (2025)

A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning
di: Song, Chih-Wei, et al.
Pubblicazione: (2024)

TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
di: He, Xixiang, et al.
Pubblicazione: (2025)

You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
di: He, Wenchong, et al.
Pubblicazione: (2025)

Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
di: Pang, Jinlong, et al.
Pubblicazione: (2025)

Phased Instruction Fine-Tuning for Large Language Models
di: Pang, Wei, et al.
Pubblicazione: (2024)

Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace
di: Zhang, Jia-Chen, et al.
Pubblicazione: (2025)

Fine-Tuning Language Models with Reward Learning on Policy
di: Lang, Hao, et al.
Pubblicazione: (2024)

Removing RLHF Protections in GPT-4 via Fine-Tuning
di: Zhan, Qiusi, et al.
Pubblicazione: (2023)

UMB@PerAnsSumm 2025: Enhancing Perspective-Aware Summarization with Prompt Optimization and Supervised Fine-Tuning
di: Qi, Kristin, et al.
Pubblicazione: (2025)

Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue
di: Fan, Shixuan, et al.
Pubblicazione: (2024)

Natural Language Fine-Tuning
di: Liu, Jia, et al.
Pubblicazione: (2024)

DynaPrompt: Dynamic Test-Time Prompt Tuning
di: Xiao, Zehao, et al.
Pubblicazione: (2025)

GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
di: Liu, Yuze, et al.
Pubblicazione: (2024)

Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows
di: Ayala, Orlando Marquez, et al.
Pubblicazione: (2025)

Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
di: Ling, Zhenqing, et al.
Pubblicazione: (2025)

Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
di: Soylu, Dilara, et al.
Pubblicazione: (2024)

Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization
di: Thulke, David, et al.
Pubblicazione: (2024)

MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
di: Lu, Yiyang, et al.
Pubblicazione: (2026)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
di: Hu, Jerry Yao-Chieh, et al.
Pubblicazione: (2024)