Copertina

The Chicken and Egg Dilemma: Co-optimizing Data and Model Configurations for LLMs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Zhiliang, Leong, Alfred Wei Lun, Ong, Shao Yong, Hemachandra, Apivich, Lau, Gregory Kang Ruey, Foo, Chuan-Sheng, Liu, Zhengyuan, Chen, Nancy F., Low, Bryan Kian Hsiang
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

PIED: Physics-Informed Experimental Design for Inverse Problems
di: Hemachandra, Apivich, et al.
Pubblicazione: (2025)

PINNACLE: PINN Adaptive ColLocation and Experimental points selection
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2024)

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
di: Chen, Zhiliang, et al.
Pubblicazione: (2025)

BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks
di: Chew, Ruth Wan Theng, et al.
Pubblicazione: (2026)

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space
di: Chen, Zhiliang, et al.
Pubblicazione: (2025)

Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2024)

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2026)

Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2024)

Data Distribution Valuation
di: Xu, Xinyi, et al.
Pubblicazione: (2024)

Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
di: Wang, Jingtan, et al.
Pubblicazione: (2024)

Uncovering Scaling Laws for Large Language Models via Inverse Problems
di: Verma, Arun, et al.
Pubblicazione: (2025)

Understanding Domain Generalization: A Noise Robustness Perspective
di: Qiao, Rui, et al.
Pubblicazione: (2024)

Self-Interested Agents in Collaborative Machine Learning: An Incentivized Adaptive Data-Centric Framework
di: Vijayan, Nithia, et al.
Pubblicazione: (2024)

Decentralized Sum-of-Nonconvex Optimization
di: Liu, Zhuanghua, et al.
Pubblicazione: (2024)

How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
di: Chen, Jiangwei, et al.
Pubblicazione: (2026)

WaterDrum: Watermarking for Data-centric Unlearning Metric
di: Lu, Xinyang, et al.
Pubblicazione: (2025)

MeMo: Memory as a Model
di: Quek, Ryan Wei Heng, et al.
Pubblicazione: (2026)

Fine-tuning Language Models with Generative Adversarial Reward Modelling
di: Yu, Zhang Ze, et al.
Pubblicazione: (2023)

Global-to-Local Support Spectrums for Language Model Explainability
di: Agussurja, Lucas, et al.
Pubblicazione: (2024)

TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees
di: Li, Weida, et al.
Pubblicazione: (2026)

Provably Adaptive Linear Approximation for the Shapley Value and Beyond
di: Li, Weida, et al.
Pubblicazione: (2026)

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
di: Liu, Zhuanghua, et al.
Pubblicazione: (2024)

Source Attribution for Large Language Model-Generated Data
di: Wang, Jingtan, et al.
Pubblicazione: (2023)

Incentivizing Time-Aware Fairness in Data Sharing
di: Chen, Jiangwei, et al.
Pubblicazione: (2025)

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
di: Zhang, Ze Yu, et al.
Pubblicazione: (2025)

Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies
di: Verma, Arun, et al.
Pubblicazione: (2024)

Data value estimation on private gradients
di: Zhou, Zijian, et al.
Pubblicazione: (2024)

Paid with Models: Optimal Contract Design for Collaborative Machine Learning
di: Wang, Bingchen, et al.
Pubblicazione: (2024)

COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents
di: Verma, Arun, et al.
Pubblicazione: (2025)

Robustifying and Boosting Training-Free Neural Architecture Search
di: He, Zhenfeng, et al.
Pubblicazione: (2024)

Is Data Shapley Not Better than Random in Data Selection? Ask NASH
di: Tian, Xiao, et al.
Pubblicazione: (2026)

TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs
di: Wang, Cheng, et al.
Pubblicazione: (2024)

BILBO: BILevel Bayesian Optimization
di: Chew, Ruth Wan Theng, et al.
Pubblicazione: (2025)

Dependency Structure Search Bayesian Optimization for Decision Making Models
di: Rajpal, Mohit, et al.
Pubblicazione: (2023)

Transient entanglement generation in driven chiral networks beyond the secular approximation
di: Foo, Yan Xi, et al.
Pubblicazione: (2026)

Active Human Feedback Collection via Neural Contextual Dueling Bandits
di: Verma, Arun, et al.
Pubblicazione: (2025)

Neural Dueling Bandits: Preference-Based Optimization with Human Feedback
di: Verma, Arun, et al.
Pubblicazione: (2024)

Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models
di: Zhang, Ze Yu, et al.
Pubblicazione: (2024)

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning
di: Tian, Xiao, et al.
Pubblicazione: (2023)

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
di: Tian, Xiao, et al.
Pubblicazione: (2026)