Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity

arXiv:2609.31505 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• In-Context Learning — 15× in this paper · explained in 4 episodes
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Prompt Engineering — 5× in this paper · explained in 4 episodes
• Semantic Similarity — 5× in this paper · explained in 2 episodes
• Explainability — 4× in this paper · explained in 3 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• AI Integration — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• In Context Learning — 1× in this paper
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Proximal Policy Optimization — 1× in this paper
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Two-Stage Optimization — 1× in this paper · explained in 2 episodes

Sources

• Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
• Language Models are Few-Shot Learners
• Efficient Prompting Methods for Large Language Models: A Survey
• Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading
• Learning to Compress Prompt in Natural Language Formats
• Towards A Rigorous Science of Interpretable Machine Learning
• Context Length Alone Hurts LLM Performance Despite Perfect Retrieval
• LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
• Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information
• The Power of Scale for Parameter-Efficient Prompt Tuning
• Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
• Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles
• A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
• The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
• Large Language Models Can Be Easily Distracted by Irrelevant Context
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
• Attention Is All You Need
• Resilience of Large Language Models for Noisy Instructions
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?

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