Key Points
1. Retrieval-Augmented Generation (RAG) allows for the supplementation of Limited Language Models (LLMs) by extending the input with external information. However, a drawback is the increase in decoding time due to the addition of more context to the input.
2. COCOM is presented as an effective context compression method that reduces long contexts to only a handful of Context Embeddings, thus speeding up the generation time while achieving higher performance compared to existing efficient context compression methods. It allows for different compression rates, trading off decoding time for answer quality.
3. The paper addresses the limitations of previous embedding-based compression methods, which often rely on large compression models to achieve high effectiveness and offer fixed compression rates that do not support handling multiple contexts for answer generation.
4. COCOM allows for handling multiple contexts more effectively and significantly reduces decoding time for long inputs, demonstrating a speed-up of up to 5.69 times while achieving higher performance compared to existing efficient context compression methods.
5. The proposed COCOM method utilizes a single model for context compression and answer generation and demonstrates significantly higher effectiveness than current context-compression approaches, achieving higher performance while handling multiple contexts more effectively.
6. The authors present results of experiments showing the impact of pre-training approaches on the effectiveness of COCOM, indicating that fine-tuning the decoder is a critical factor that strongly impacts the performance of the model.
7. The experiments demonstrate that training the context compressor model jointly with the decoder model leads to significantly higher effectiveness compared to other methods, thus highlighting the importance of tuning all components for context compression.
8. Context compression significantly reduces the answer generation time, GPU memory, and the number of operations per generated token, providing a more efficient approach compared to RAG without compression.
9. The results show that COCOM effectively compresses multiple contexts into context embeddings, providing a favorable trade-off between efficiency and effectiveness and demonstrating a potential reduction in the computational footprint of RAG models.
Summary
The paper introduces COCOM, an effective context compression method for Retrieval-Augmented Generation (RAG) models. RAG systems extend the input to large language models (LLMs) with additional relevant context from external sources, which can significantly improve performance on knowledge-intensive tasks. However, this addition of context dramatically increases the input length, slowing down decoding time during inference.
To address this challenge, the paper presents COCOM, which compresses long contexts into a small set of context embeddings. This compression allows the RAG model to maintain high performance while dramatically reducing inference time. COCOM uses the same model for both context compression and answer generation, allowing it to jointly learn how to effectively compress the context and leverage the compressed representations during decoding.
The paper highlights several key advantages of COCOM over prior context compression methods:
1. COCOM allows for adaptable compression rates, enabling users to control the trade-off between inference speed and answer quality. Experiments show COCOM can reduce inference time by up to 5.69x while maintaining high performance. 2. COCOM can effectively handle multiple contexts, which is important for tasks requiring reasoning over information from multiple sources. Prior methods were limited to single contexts. 3. COCOM fine-tunes the entire model, including the decoder, on the target task, whereas prior methods only tuned the compression module and used a frozen decoder.
The paper demonstrates the importance of full model tuning for high effectiveness. Through extensive experiments on multiple QA datasets, the paper shows COCOM significantly outperforms prior context compression methods in terms of both effectiveness and efficiency.
The paper also conducts detailed analysis to understand the factors driving COCOM's performance, including the impact of pre-training, fine-tuning data, and decoder tuning.
Overall, the paper presents a novel and effective approach to context compression for RAG models, highlighting the ability to dramatically reduce inference time while maintaining high performance, and offers insights on key factors for successful compression. The work demonstrates the potential to deploy RAG models more efficiently in real-world applications.
Reference: https://arxiv.org/abs/2407.09252