How Output Format Confounds Data Quality and Capability in Instruction Tuning

arXiv:2609.02015 · cs.CL · Submitted 2026-09-02 · Read on arXiv

cs.CL

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/tatsu-lab/stanford_alpaca

Terminology

• Alignment — 99× in this paper · explained in 1 episode
• AUC — 45× in this paper · explained in 2 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• Instruction Tuning — 8× in this paper · explained in 2 episodes
• Alignment Scores — 7× in this paper · explained in 1 episode
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• International Conference on Machine Learning — 1× in this paper
• Scaffolding — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Mistral 7B
• Impossibility Theorems for Feature Attribution
• GIST: Targeted Data Selection for Instruction Tuning via Coupled Optimization Geometry
• Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
• Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
• Qwen3 Technical Report

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