Learning a Single Token to Replace Long System Prompts in LLMs

arXiv:2511.23271 · cs.CL · Submitted 2025-11-28 · Read on arXiv

cs.CL

Submitted: 2025-11-28

Updated: 2026-08-28

Code: https://github.com/meta-llama/llama-models

Terminology

• Distillation — 15× in this paper · explained in 2 episodes
• Alignment — 14× in this paper · explained in 1 episode
• Knowledge Distillation — 8× in this paper · explained in 11 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Agentic AI — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Harry Potter Dialogue — 1× in this paper
• Instruction Tuning — 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 6 episodes
• Pre-training Context Compressor — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• The Llama 3 Herd of Models
• LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
• You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors
• Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
• Exploring the Hidden Capacity of LLMs for One-Step Text Generation
• Saliency-driven Dynamic Token Pruning 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