What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

arXiv:2604.25132 · cs.CL · Submitted 2026-04-28 · Read on arXiv

cs.CL

Submitted: 2026-04-28

Updated: 2026-10-15

Comments: ACL 2026, main conference

Code: https://github.com/trust-nlp/SyntheticData-Curator

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Instruction Tuning — 30× in this paper · explained in 3 episodes
• In-context Influence — 29× in this paper
• Benchmark — 21× in this paper · explained in 2 episodes
• In-Context Learning — 18× in this paper · explained in 7 episodes
• Fine-Tuning — 12× in this paper · explained in 5 episodes
• Papers — 10× in this paper · explained in 3 episodes
• Redundancy — 8× in this paper · explained in 2 episodes
• Clustering — 7× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Perplexity — 5× in this paper · explained in 3 episodes
• Semantic Clustering — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation Study — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• In-Context Learning (ICL) — 2× in this paper · explained in 6 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Win Rate — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Influence Function Direction — 1× in this paper
• K-means Clustering — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes
• National Science Foundation — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Position Bias — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 5 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• Zero-shot performance — 1× in this paper · explained in 2 episodes

Sources

• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• The Llama 3 Herd of Models
• Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
• Tulu 3: Pushing Frontiers in Open Language Model Post-Training
• Instruction Tuning with GPT-4
• Proximal Policy Optimization Algorithms
• Cultivating Multidisciplinary AI Workforce Development on iTiger GPU Cluster: Practices and Challenges
• Diversity Measurement and Subset Selection for Instruction Tuning Datasets
• WizardLM: Empowering large pre-trained language models to follow complex instructions
• Entropy Law: The Story Behind Data Compression and LLM Performance
• The Best Instruction-Tuning Data are Those That Fit
• Instruction-Following Evaluation for Large Language Models

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