Copertina

Diversity-oriented Data Augmentation with Large Language Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Zaitian, Zhang, Jinghan, Zhang, Xinhao, Liu, Kunpeng, Wang, Pengfei, Zhou, Yuanchun
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dynamic and Adaptive Feature Generation with LLM
di: Zhang, Xinhao, et al.
Pubblicazione: (2024)

A Comprehensive Survey on Data Augmentation
di: Wang, Zaitian, et al.
Pubblicazione: (2024)

Prototypical Reward Network for Data-Efficient RLHF
di: Zhang, Jinghan, et al.
Pubblicazione: (2024)

CataLM: Empowering Catalyst Design Through Large Language Models
di: Wang, Ludi, et al.
Pubblicazione: (2024)

DavIR: Data Selection via Implicit Reward for Large Language Models
di: Zhou, Haotian, et al.
Pubblicazione: (2023)

Benchmarking Benchmark Leakage in Large Language Models
di: Xu, Ruijie, et al.
Pubblicazione: (2024)

Deep Cut-informed Graph Embedding and Clustering
di: Ning, Zhiyuan, et al.
Pubblicazione: (2025)

Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
di: Chen, Sijia, et al.
Pubblicazione: (2024)

RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
di: Liu, Wanlong, et al.
Pubblicazione: (2024)

Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models
di: Wang, Jiankun, et al.
Pubblicazione: (2024)

Beware of Calibration Data for Pruning Large Language Models
di: Ji, Yixin, et al.
Pubblicazione: (2024)

MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation
di: Wang, Xiaohan, et al.
Pubblicazione: (2024)

Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
di: Tao, Yongding, et al.
Pubblicazione: (2025)

Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
di: Zhang, Jinghan, et al.
Pubblicazione: (2024)

GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models
di: Niu, Peizhi, et al.
Pubblicazione: (2025)

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
di: Havrilla, Alex, et al.
Pubblicazione: (2024)

Offset Unlearning for Large Language Models
di: Huang, James Y., et al.
Pubblicazione: (2024)

Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models
di: Cui, Andrea Yaoyun, et al.
Pubblicazione: (2025)

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
di: Dong, Guanting, et al.
Pubblicazione: (2023)

Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
di: Zhang, Bryan, et al.
Pubblicazione: (2024)

Boosting Large Language Models with Mask Fine-Tuning
di: Zhang, Mingyuan, et al.
Pubblicazione: (2025)

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
di: Zhou, Chenxi, et al.
Pubblicazione: (2025)

Large Language Models as Optimizers
di: Yang, Chengrun, et al.
Pubblicazione: (2023)

Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
di: Zhou, Fan, et al.
Pubblicazione: (2024)

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
di: Jin, Zhuoran, et al.
Pubblicazione: (2024)

The Role of Diversity in In-Context Learning for Large Language Models
di: Xiao, Wenyang, et al.
Pubblicazione: (2025)

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
di: Shi, Weijie, et al.
Pubblicazione: (2025)

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
di: Chen, Zhixun, et al.
Pubblicazione: (2025)

Echo: A Large Language Model with Temporal Episodic Memory
di: Liu, WenTao, et al.
Pubblicazione: (2025)

Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models
di: Wang, Fei, et al.
Pubblicazione: (2024)

Instruction Tuning for Large Language Models: A Survey
di: Zhang, Shengyu, et al.
Pubblicazione: (2023)

Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
di: Zhang, Yanxiang, et al.
Pubblicazione: (2025)

Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
di: Yang, Jinluan, et al.
Pubblicazione: (2025)

Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data
di: Yang, Shiping, et al.
Pubblicazione: (2025)

LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
di: Yang, Runming, et al.
Pubblicazione: (2024)

MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
di: Ha, Hyeonjeong, et al.
Pubblicazione: (2026)

Fine-tuning can Help Detect Pretraining Data from Large Language Models
di: Zhang, Hengxiang, et al.
Pubblicazione: (2024)

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
di: Liu, Xiaoze, et al.
Pubblicazione: (2024)

Rethinking Data Mixing from the Perspective of Large Language Models
di: Xu, Yuanjian, et al.
Pubblicazione: (2026)

Large Language Models as Tool Makers
di: Cai, Tianle, et al.
Pubblicazione: (2023)