PC-SubMax: Efficient Prompt Compression via Regularized Submodular Maximization

arXiv:2609.32474 · cs.CL · Submitted 2026-09-26 · Read on arXiv

cs.CL

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Benchmark — 12× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Redundancy — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Perplexity — 4× in this paper · explained in 3 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Query Complexity — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• IEEE Transactions on Knowledge and Data Engineering — 2× in this paper
• Information Gain — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Combinatorial Optimization — 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
• Exact Match — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• In International Conference on Machine Learning — 1× in this paper
• In-Context Learning — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Training Verifiers to Solve Math Word Problems
• In-context Autoencoder for Context Compression in a Large Language Model
• RULER: What's the Real Context Size of Your Long-Context Language Models?
• DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
• CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
• Read As Human: Compressing Context via Parallelizable Close Reading and Skimming
• COMI: Coarse-to-fine Context Compression via Marginal Information Gain
• LLaMA: Open and Efficient Foundation Language Models

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