Efficient Long Context Language Model Retrieval with Compression (2412.18232v2)

Abstract: Long Context LLMs (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods. However, processing a large number of passages within in-context for retrieval is computationally expensive, and handling their representations during inference further exacerbates the processing time; thus, we aim to make LCLM retrieval more efficient and potentially more effective with passage compression. Specifically, we propose a new compression approach tailored for LCLM retrieval, which is trained to maximize the retrieval performance while minimizing the length of the compressed passages. To accomplish this, we generate the synthetic data, where compressed passages are automatically created and labeled as chosen or rejected according to their retrieval success for a given query, and we train the proposed Compression model for Long context Retrieval (CoLoR) with this data via preference optimization while adding the length regularization loss on top of it to enforce brevity. Through extensive experiments on 9 datasets, we show that CoLoR improves the retrieval performance by 6% while compressing the in-context size by a factor of 1.91. Our code is available at: https://github.com/going-doer/CoLoR.

• The paper introduces a novel passage compression method to improve the efficiency and effectiveness of Long Context Language Model (LCLM) retrieval systems.
• The authors propose CoLoR, a compression model trained using synthetic data and preference optimization, designed to enhance retrieval accuracy while significantly reducing input size.
• Results show CoLoR improves retrieval performance by 6% and reduces in-context size by nearly half across nine datasets, enabling more efficient and scalable LCLM applications.

Efficient Long Context LLM Retrieval with Compression: An Expert Perspective

The paper "Efficient Long Context LLM Retrieval with Compression" addresses a significant challenge in the deployment of Long Context LLMs (LCLMs) for Information Retrieval (IR): the computational cost associated with processing extensive textual contexts. The authors propose an innovative method that leverages passage compression to enhance the efficiency and potential effectiveness of LCLM-based retrieval systems.

Key Contributions

1. Passage Compression for LCLM Retrieval: The core contribution of this paper is a novel compression approach designed specifically for LCLM-based retrieval tasks. This is not a mere length reduction strategy but an optimization process intended to enhance retrieval accuracy while minimizing the input size.
2. Synthetic Data Generation for Training: The authors adopt an unconventional method for training their compression model by generating synthetic data. Here, compressed passages are automatically labeled based on their retrieval success, aligning the training objective with practical outcomes in retrieval tasks.
3. CoLoR – Compression Model for Long-Context Retrieval: The proposed Compression model for Long-context Retrieval (CoLoR) incorporates preference optimization techniques combined with length regularization to enforce brevity. CoLoR demonstrates improved retrieval performance with a significantly compressed input size.

Results and Implications

The results, validated on nine datasets, reveal that CoLoR enhances retrieval performance by 6% while reducing the in-context size by a factor of 1.91. This improvement suggests a dual advantage: enhanced retrieval performance and reduced computational load.

1. Improved Efficiency: By reducing the length of passages processed by LCLMs, CoLoR directly addresses the computational challenges associated with long contexts. This efficiency gain is crucial for real-world applications where large-scale retrieval is necessary.
2. Enhanced Retrieval Accuracy: The integration of preference-based learning ensures that compression does not result in a loss of critical information pertinent to the retrieval task. This balance between efficiency and accuracy is pivotal for practical applications.
3. Generalizability: The authors demonstrate CoLoR's applicability across different datasets and retrieval scenarios, suggesting a versatile approach that can be adapted beyond the immediate scope of the paper.

Theoretical and Practical Implications

From a theoretical perspective, the integration of preference optimization with passage compression introduces a new dimension to IR tasks, bridging the gap between text compression and retrieval accuracy. This method challenges traditional paradigms, which often treat compression and retrieval as separate tasks.

Practically, CoLoR can significantly impact industries reliant on large-scale data retrieval, such as legal tech, academic research, and digital libraries. The ability to process more data efficiently without sacrificing precision can lead to more responsive and scalable systems.

Future Directions

The paper opens several avenues for future research. First, exploring the applicability of CoLoR in other domains requiring long-context processing, such as conversational AI and document summarization, could be fruitful. Additionally, further enhancing the compression mechanism to dynamically adjust based on the complexity and nature of the query and passages might offer even more nuanced capabilities. Lastly, integrating CoLoR with real-time systems to assess its performance in dynamic environments remains an exciting prospect.

In conclusion, this paper presents a comprehensive approach to improving LCLM-based retrieval through efficient compression, paving the way for more agile and effective information processing systems. Its implications for both theoretical advancements and practical applications underscore its significance in the field of computer science research.