LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Core Concepts

Efficient prompt compression through data distillation improves generalizability and efficiency.

Abstract

This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. It introduces a data distillation procedure to compress prompts without losing crucial information, leading to lower latency with smaller models. The approach is evaluated on various datasets, showing significant performance gains over strong baselines.

Directory:

1. Abstract
   • Focuses on task-agnostic prompt compression.
   • Challenges in existing methods.
2. Introduction
   • Emergence of prompting techniques for LLMs.
   • Benefits of prompt compression.
3. Dataset Construction
   • Data distillation procedure.
   • Data annotation algorithm.
4. Quality Control
   • Metrics for assessing compressed texts and annotations.
5. Compressor
   • Formulating prompt compression as a token classification problem.
6. Experiment
   • Implementation details and evaluation metrics.
7. Results
   • Performance comparison with baselines on in-domain and out-of-domain benchmarks.
8. Conclusion
   • Summary of findings and limitations.

Stats

Our model is 3x-6x faster than existing prompt compression methods, accelerating end-to-end latency by 1.6x-2.9x with compression ratios of 2x-5x.

Quotes

"Prompt compression is a straightforward solution to address issues related to increased computational overhead."
"Our approach leads to lower latency by explicitly learning the compression objective with smaller models."

Key Insights Distilled From

LLMLingua-2

by Zhuo... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12968.pdf

Deeper Inquiries

How can the dataset construction process be improved to enhance generalization across different domains

To enhance generalization across different domains in dataset construction for prompt compression, several improvements can be implemented.

Diverse Data Sources: Instead of relying solely on data from a single domain like meeting transcripts, incorporating diverse sources such as news articles, scientific papers, social media posts, and more can help capture a broader range of language patterns and contexts.

Domain Adaptation Techniques: Employing domain adaptation techniques to fine-tune the model on data from various domains can improve its ability to generalize across different types of text.

Data Augmentation: Generating synthetic examples by applying transformations like paraphrasing, word substitutions, or adding noise to existing samples can increase the diversity within the dataset and expose the model to a wider range of linguistic variations.

Transfer Learning: Pre-training the model on a large-scale generic corpus before fine-tuning it on domain-specific data can help in transferring knowledge learned from one domain to another effectively.

Cross-Domain Evaluation Metrics: Using evaluation metrics that are agnostic to specific domains but focus on overall performance and generalizability can provide insights into how well the model performs across diverse datasets.

What are the implications of using bidirectional context-aware feature extractors in prompt compression models

The implications of using bidirectional context-aware feature extractors in prompt compression models are significant:

Improved Information Capture: Bidirectional models have access to both preceding and succeeding tokens during encoding, enabling them to capture richer contextual information compared to unidirectional models.

Enhanced Faithfulness: By leveraging bidirectional context-aware features, prompt compression models can better preserve essential information while discarding redundant details since they have a comprehensive understanding of token importance based on their surrounding context.

Better Generalization: Models utilizing bidirectional context-aware features tend to perform well across various tasks and datasets due to their ability to comprehend complex relationships within text comprehensively.

Reduced Information Loss: Bidirectional feature extraction helps in minimizing information loss during compression by considering all relevant tokens around each word when making decisions about preservation or removal.

How can the findings from this study be applied to improve other natural language processing tasks beyond prompt compression

The findings from this study hold potential for improving various natural language processing (NLP) tasks beyond prompt compression:

Summarization Tasks: The approach's focus on retaining essential information while compressing prompts could be applied directly in abstractive summarization tasks where generating concise summaries without losing key details is crucial.

Document Understanding: Leveraging similar token classification methods could aid in extracting important sections or sentences from lengthy documents for improved document understanding applications.

3.Machine Translation: Implementing similar strategies could assist in enhancing translation quality by focusing on preserving critical content during sentence transformation processes.
4Information Retrieval: By prioritizing important words through token classification techniques derived from this study, search engines could better understand user queries and retrieve relevant results efficiently.