Context Embeddings for Efficient Answer Generation in RAG

Original Paper: https://arxiv.org/abs/2407.09252

By: David Rau, Shuai Wang, Hervé Déjean, Stéphane Clinchant

Abstract

Retrieval-Augmented Generation (RAG) allows for overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer which slows down decoding time directly translating to the time a user has to wait for an answer. We address this challenge by presenting COCOM, an effective context compression method, reducing long contexts to only a handful of Context Embeddings speeding up the generation time by a large margin. Our method allows for different compression rates trading off decoding time for answer quality. Compared to earlier methods, COCOM allows for handling multiple contexts more effectively, significantly reducing decoding time for long inputs. Our method demonstrates a speed-up of up to 5.69x while achieving higher performance compared to existing efficient context compression methods.

Summary Notes

Figure. Overview of our COCOM (-light) model pipeline.

In the realm of large language models (LLMs), the capability to generate accurate answers from vast amounts of data is a marvel.

However, this prowess comes with a significant computational cost, especially when dealing with extensive contextual inputs.

The recent research by Rau et al. introduces an innovative method called COCOM (COntext COmpression Model) to tackle this challenge, significantly enhancing the efficiency of Retrieval-Augmented Generation (RAG) systems.

Introduction: The Need for Speed in RAG

RAG systems extend the input to LLMs by retrieving relevant documents from external sources.

This augmentation, while enhancing answer accuracy, also increases the length of the contextual input, thereby slowing down the decoding process.

Users are often left waiting for responses as the model sifts through long contexts.

COCOM addresses this bottleneck by compressing these long contexts into a manageable number of context embeddings, dramatically speeding up the generation time.

Methodology: Compressing Contexts with COCOM

The core of COCOM lies in its ability to reduce extensive input contexts into a small set of context embeddings.

This method allows for different compression rates, effectively trading off between decoding time and answer quality. Here's a breakdown of the key methodologies employed:

1. Context Compression: COCOM compresses the input context into embeddings, significantly reducing the input size while maintaining essential information. This is achieved by training a single model for both context compression and answer generation, ensuring seamless integration and efficient processing.
2. Adaptable Compression Rate: The compression rate can be varied, allowing the model to balance between higher answer quality and faster generation times. For instance, compressing a context of length 128 tokens at a rate of 64 yields just two context embeddings, reducing the input size by 64 times.
3. Handling Multiple Contexts: Unlike previous methods limited to single-document contexts, COCOM can handle multiple contexts simultaneously. This capability is particularly beneficial for knowledge-intensive tasks requiring reasoning over several documents.

Training and Fine-Tuning

To achieve effective context compression, the model undergoes pre-training on auto-encoding tasks and language modeling from context embeddings.

This helps the model learn to compress and decompress input effectively.

For fine-tuning, the model is trained on a combination of publicly available QA datasets, enhancing its ability to generate accurate answers from compressed contexts.

Findings: Speed and Performance

COCOM demonstrates a significant speed-up in answer generation. For instance, it reduces inference time by up to 5.69 times and computational operations (GFLOPs) by up to 22 times compared to traditional RAG systems without compression.

The model achieves this efficiency while maintaining high performance, often outperforming existing context compression methods.

Key Results

• Effectiveness: COCOM shows a substantial improvement in Exact Match (EM) scores across various datasets. For example, at a compression rate of 4, it achieves an average EM score of 0.585, significantly higher than other methods.
• Efficiency: The model reduces answer generation time and GPU memory usage drastically. For example, at a compression rate of 16, it reduces the answer generation time to 213 ms (a 5 times reduction) and GFLOPs to 2465 (a 10 times reduction).

Real-World Implications

The implications of COCOM are profound for real-world applications of RAG systems:

1. Faster Response Times: By compressing contexts efficiently, COCOM reduces the time users have to wait for responses, enhancing user experience in applications like chatbots and virtual assistants.
2. Cost-Effective Deployment: The significant reduction in computational operations and memory usage translates to lower operational costs, making it feasible to deploy powerful RAG systems even in resource-constrained environments.
3. Scalability: The ability to handle multiple contexts efficiently makes COCOM scalable for complex tasks requiring extensive knowledge and reasoning, such as legal document analysis and scientific research.

Conclusion: A Leap Towards Efficient AI

COCOM represents a significant leap towards making LLM-based systems more efficient and scalable.

Compressing contexts into manageable embeddings, not only speeds up the generation process but also maintains high answer quality.

This balance of efficiency and effectiveness makes COCOM a promising solution for the future of AI-driven applications.

As we look ahead, further research could explore the potential of COCOM in multilingual and diverse task settings, extending its benefits across various domains.

The advancements in context compression techniques like COCOM are set to redefine the capabilities and applications of AI in our daily lives.