Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv:2608.26779 · cs.CL · Submitted 2026-08-27 · Read on arXiv

cs.CL

Submitted: 2026-08-27

Updated: 2026-08-27

Comments: Preprint

Code: https://github.com/01choco/instruction-refinement

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

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Alignment — 20× in this paper · explained in 1 episode
• Core — 12× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• LLM-as-a-Judge — 4× in this paper · explained in 3 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Instruction-Following Difficulty — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Sources

• Less is More: Improving LLM Alignment via Preference Data Selection
• Impact of Preference Noise on the Alignment Performance of Generative Language Models
• The Llama 3 Herd of Models
• Uncertainty-Penalized Direct Preference Optimization
• Constitutional AI: Harmlessness from AI Feedback
• From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
• Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning
• DETAIL Matters: Measuring the Impact of Prompt Specificity on Reasoning in Large Language Models
• Alignment Data Map for Efficient Preference Data Selection and Diagnosis
• Red Teaming Language Models with Language Models
• Holistic Evaluation of Language Models
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
• Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following Capability
• WizardLM: Empowering large pre-trained language models to follow complex instructions
• UltraMedical: Building Specialized Generalists in Biomedicine
• Instruction-Following Evaluation for Large Language Models

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering