Dynamic Compressing Prompts for Efficient Inference of Large Language Models

• URL: http://arxiv.org/abs/2504.11004v1
• Date: Tue, 15 Apr 2025 09:20:45 GMT
• Title: Dynamic Compressing Prompts for Efficient Inference of Large Language Models
• Authors: Jinwu Hu, Wei Zhang, Yufeng Wang, Yu Hu, Bin Xiao, Mingkui Tan, Qing Du,
• Abstract summary: Large Language Models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques.<n>While prompt compression is a straightforward solution, existing methods confront the challenges of retaining essential information, adapting to context changes, and remaining effective across different tasks.<n>Our method reduces the number of prompt tokens while aiming to preserve the performance as much as possible.
• Score: 38.604760935983364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques. However, these techniques often require lengthy prompts, which increase computational costs and can hinder performance because of the limited context windows of LLMs. While prompt compression is a straightforward solution, existing methods confront the challenges of retaining essential information, adapting to context changes, and remaining effective across different tasks. To tackle these issues, we propose a task-agnostic method called Dynamic Compressing Prompts (LLM-DCP). Our method reduces the number of prompt tokens while aiming to preserve the performance as much as possible. We model prompt compression as a Markov Decision Process (MDP), enabling the DCP-Agent to sequentially remove redundant tokens by adapting to dynamic contexts and retaining crucial content. We develop a reward function for training the DCP-Agent that balances the compression rate, the quality of the LLM output, and the retention of key information. This allows for prompt token reduction without needing an external black-box LLM. Inspired by the progressive difficulty adjustment in curriculum learning, we introduce a Hierarchical Prompt Compression (HPC) training strategy that gradually increases the compression difficulty, enabling the DCP-Agent to learn an effective compression method that maintains information integrity. Experiments demonstrate that our method outperforms state-of-the-art techniques, especially at higher compression rates. The code for our approach will be available at https://github.com/Fhujinwu/DCP.

Related papers

• Understanding and Improving Information Preservation in Prompt Compression for LLMs [10.912320980464571]
  In information-intensive tasks, the prompt length can grow fast, leading to increased computational requirements, performance degradation, and induced biases from irrelevant or redundant information.<n>We propose a holistic evaluation framework that allows for in-depth analysis of prompt compression methods.
  arXiv  Detail & Related papers  (2025-03-24T20:06:11Z)
• ICPC: In-context Prompt Compression with Faster Inference [0.0]
  We propose I CPC (In-context Prompt Compression), a novel and scalable prompt compression method that adaptively reduces the prompt length.<n>The key idea of I CPC is to calculate the probability of each word appearing in the prompt using encoders and calculate information carried by each word through the information function.<n> Empirically, we demonstrate that I CPC can effectively compress long texts of different categories and thus achieve better performance and speed on different types of NLP tasks.
  arXiv  Detail & Related papers  (2025-01-03T03:46:51Z)
• Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability [67.77534983324229]
  In this paper, we investigate the ability of Large Language Models to develop a unified compression method that discretizes uninformative tokens. Experiments show Selection-p achieves state-of-the-art performance across numerous classification tasks. It exhibits superior transferability to different models compared to prior work.
  arXiv  Detail & Related papers  (2024-10-15T17:05:25Z)
• TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning [11.167198972934736]
  Large language models (LLMs) such as GPT-4 have led to a surge in the size of prompts required for optimal performance.<n>We propose a novel and efficient reinforcement learning (RL) based task-aware prompt compression method.<n>We demonstrate that our RL-guided compression method improves the task performance by 8% - 189% over state-of-the-art compression techniques.
  arXiv  Detail & Related papers  (2024-09-19T18:11:59Z)
• LanguaShrink: Reducing Token Overhead with Psycholinguistics [8.123272461141815]
  LanguaShrink is a prompt compression framework for large language models. It reduces prompt length while preserving essential information. Compared to existing prompt compression methods, LanguaShrink improves end-to-end latency by 1.43 times.
  arXiv  Detail & Related papers  (2024-09-01T22:09:20Z)
• PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer [76.39111896665585]
  Incremental Learning (IL) aims to learn deep models on sequential tasks continually. Recent vast pre-trained models (PTMs) have achieved outstanding performance by prompt technique in practical IL without the old samples.
  arXiv  Detail & Related papers  (2024-07-04T10:37:58Z)
• Compressing LLMs: The Truth is Rarely Pure and Never Simple [90.05366363633568]
  Knowledge-Intensive Compressed LLM BenchmarK aims to redefine the evaluation protocol for compressed Large Language Models. LLM-KICK unveils many favorable merits and unfortunate plights of current SoTA compression methods. LLM-KICK is designed to holistically access compressed LLMs' ability for language understanding, reasoning, generation, in-context retrieval, in-context summarization, etc.
  arXiv  Detail & Related papers  (2023-10-02T17:42:37Z)
• Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications [63.29358103217275]
  Large Language Models (LLMs) often leads to reduced performance, especially for knowledge-intensive tasks. We propose two conjectures on the nature of the damage: one is certain knowledge being forgotten (or erased) after compression. We introduce a variant called Inference-time Dynamic Prompting (IDP) that can effectively increase prompt diversity without incurring any inference overhead.
  arXiv  Detail & Related papers  (2023-10-02T03:12:06Z)
• Discrete Prompt Compression with Reinforcement Learning [2.664293070994717]
  Compressed prompts aid instruction-tuned language models (LMs) in overcoming context window limitations and reducing computational costs. Existing methods, which primarily based on training embeddings, face various challenges associated with interpretability, the fixed number of embedding tokens, reusability across different LMs, and inapplicability when interacting with black-box APIs. This study proposes prompt compression with reinforcement learning (PCRL), which is a discrete prompt compression method that addresses these issues.
  arXiv  Detail & Related papers  (2023-08-17T03:10:17Z)
• Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt [96.24800696597707]
  We introduce a new perspective to optimize this trade-off by prompting compressed models. We propose a soft prompt learning method where we expose the compressed model to the prompt learning process. Our experimental analysis suggests our soft prompt strategy greatly improves the performance of the 8x compressed LLaMA-7B model.
  arXiv  Detail & Related papers  (2023-05-17T20:45:13Z)
• RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
  This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL) RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs) Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
  arXiv  Detail & Related papers  (2022-05-25T07:50:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.