Cover Image

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Wei, Zeng, Weihao, He, Keqing, Jiang, Yong, He, Junxian
Format:	Preprint
Published:	2023
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild
by: Zeng, Weihao, et al.
Published: (2025)

From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning
by: Huang, Yuzhen, et al.
Published: (2025)

Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
by: Li, Ming, et al.
Published: (2024)

Parameter Efficient Instruction Tuning: An Empirical Study
by: He, Pengfei
Published: (2024)

Instruction Mining: Instruction Data Selection for Tuning Large Language Models
by: Cao, Yihan, et al.
Published: (2023)

Federated Data-Efficient Instruction Tuning for Large Language Models
by: Qin, Zhen, et al.
Published: (2024)

LESS: Selecting Influential Data for Targeted Instruction Tuning
by: Xia, Mengzhou, et al.
Published: (2024)

Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification
by: Zeng, Weihao, et al.
Published: (2025)

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
by: Zeng, Weihao, et al.
Published: (2024)

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning
by: Wu, Yang, et al.
Published: (2024)

How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
by: Wang, Yejie, et al.
Published: (2024)

Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
by: Yuan, Zhihang, et al.
Published: (2026)

DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
by: Wang, Yejie, et al.
Published: (2024)

TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data
by: Zhang, Jipeng, et al.
Published: (2024)

Less is More: Improving LLM Alignment via Preference Data Selection
by: Deng, Xun, et al.
Published: (2025)

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

Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
by: Wang, Fangxin, et al.
Published: (2026)

Data Selection for Multi-turn Dialogue Instruction Tuning
by: Li, Bo, et al.
Published: (2026)

MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space
by: Chen, Yicheng, et al.
Published: (2025)

The Best Instruction-Tuning Data are Those That Fit
by: Zhang, Dylan, et al.
Published: (2025)

The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
by: He, Bingxiang, et al.
Published: (2024)

What Makes a Reward Model a Good Teacher? An Optimization Perspective
by: Razin, Noam, et al.
Published: (2025)

Not All Documents Are What You Need for Extracting Instruction Tuning Data
by: Zhang, Chi, et al.
Published: (2025)

SMART: Submodular Data Mixture Strategy for Instruction Tuning
by: Renduchintala, H S V N S Kowndinya, et al.
Published: (2024)

AgentRefine: Enhancing Agent Generalization through Refinement Tuning
by: Fu, Dayuan, et al.
Published: (2025)

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
by: Yang, Cehao, et al.
Published: (2025)

Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
by: Gao, Chengqian, et al.
Published: (2025)

Automatic Instruction Evolving for Large Language Models
by: Zeng, Weihao, et al.
Published: (2024)

SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
by: Liu, Liangxin, et al.
Published: (2024)

Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment after Instruction Tuning
by: Li, Guanlin, et al.
Published: (2025)

From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
by: Wang, Fei, et al.
Published: (2024)

Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
by: Zhou, Hang, et al.
Published: (2024)

What Shapes a Creative Machine Mind? Comprehensively Benchmarking Creativity in Foundation Models
by: He, Zicong, et al.
Published: (2025)

What is in Your Safe Data? Identifying Benign Data that Breaks Safety
by: He, Luxi, et al.
Published: (2024)

VISPA: Pluralistic Alignment via Automatic Value Selection and Activation
by: Zheng, Shenyan, et al.
Published: (2026)

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
by: Dai, Qirun, et al.
Published: (2025)

Neural Networks for Learnable and Scalable Influence Estimation of Instruction Fine-Tuning Data
by: Agarwal, Ishika, et al.
Published: (2025)

Selection of LLM Fine-Tuning Data based on Orthogonal Rules
by: Li, Xiaomin, et al.
Published: (2024)

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

Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder
by: Yang, Xianjun, et al.
Published: (2025)