PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression (2504.16574v1)

Abstract: LLMs have achieved remarkable progress, demonstrating unprecedented capabilities across various natural language processing tasks. However, the high costs associated with such exceptional performance limit the widespread adoption of LLMs, highlighting the need for prompt compression. Existing prompt compression methods primarily rely on heuristic truncation or abstractive summarization techniques, which fundamentally overlook the intrinsic mechanisms of LLMs and lack a systematic evaluation of token importance for generation. In this work, we introduce Prompt Importance Sampling (PIS), a novel compression framework that dynamically compresses prompts by sampling important tokens based on the analysis of attention scores of hidden states. PIS employs a dual-level compression mechanism: 1) at the token level, we quantify saliency using LLM-native attention scores and implement adaptive compression through a lightweight 9-layer reinforcement learning (RL) network; 2) at the semantic level, we propose a Russian roulette sampling strategy for sentence-level importance sampling. Comprehensive evaluations across multiple domain benchmarks demonstrate that our method achieves state-of-the-art compression performance. Notably, our framework serendipitously enhances reasoning efficiency through optimized context structuring. This work advances prompt engineering by offering both theoretical grounding and practical efficiency in context management for LLMs.

Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression

The research paper introduces a novel approach named Prompt Importance Sampling (PIS), which aims to enhance the efficiency of prompt compression in LLMs. LLMs are notorious for their high computational demand due to extensive token sequences, especially in complex tasks such as reasoning and planning. This work addresses computational limitations by proposing a method that improves the balance between token reduction and semantic fidelity through a well-founded approach that leverages the attention mechanisms inherent in LLMs.

Overview of the PIS Methodology

Prompt Importance Sampling (PIS) is designed to decrease computational overhead by compressing input prompts while preserving essential semantic information. The authors propose a dual-level compression mechanism:

• Token-Level Compression: This aspect of the PIS framework utilizes attention scores from hidden states to determine token importance. The authors employ a nine-layer reinforcement learning (RL) network to adaptively compress tokens based on their saliency. Attention scores serve as a proxy for importance weights, streamlining the compression by prioritizing tokens that contribute most significantly to the LLM's output distribution.
• Sentence-Level Compression: The paper introduces a Russian roulette sampling strategy at the sentence level to tackle semantic redundancy among sentences. This probabilistic approach ensures that sentences with overlapping or low-contribution semantics are more likely to be discarded, thus optimizing context structuring for efficiency.

By integrating importance sampling directly with the LLM's native attention mechanism, PIS improves reasoning efficiency and decreases inference latency, offering both practical and theoretical advancements in prompt engineering.

Numerical Results and Claims

The authors provide extensive empirical evaluations across multiple domain-specific benchmarks, demonstrating that PIS achieves state-of-the-art compression performance. Notably:

1. PIS yields a 15% improvement in task performance at equivalent compression ratios compared to existing methods.
2. The framework reduces inference overhead by 38% when benchmarked against strong baseline compression approaches.
3. PIS not only maintains but enhances reasoning efficiency, showing a 5% increase in accuracy on downstream tasks using compressed prompts versus original inputs.

These results underscore the efficacy of the proposed approach in maximizing the efficiency of LLM operations without compromising the integrity and quality of generated outputs.

Implications and Future Directions

The implications of this research are manifold. Practically, PIS provides a path towards resource-efficient LLM deployment, enabling wider accessibility and application by reducing computational demands. Theoretically, it serves as a foundational development in contextual optimization for LLMs, illustrating a method that synergizes model-specific mechanisms like attention with classic trick like importance sampling for optimal prompt engineering.

Future developments could further investigate alternative architectures for RL networks that might better optimize compression decisions or explore more sophisticated sampling techniques for sentence-level reduction. Additionally, expanding the versatility and robustness of PIS in diverse real-world scenarios remains an imperative task, potentially examining cross-domain and multilingual applications.

This work contributes to the ongoing advancement of artificial intelligence systems, offering a balanced approach to the challenges posed by LLM complexity and computational constraints. By harnessing attention mechanisms and importance sampling, the PIS framework represents a significant step towards streamlined and effective prompt management within LLMs.