Inference Scaling for Long-Context Retrieval Augmented Generation

Abstract: The scaling of inference computation has unlocked the potential of long-context LLMs across diverse settings. For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance. In this work, we investigate inference scaling for retrieval augmented generation (RAG), exploring the combination of multiple strategies beyond simply increasing the quantity of knowledge, including in-context learning and iterative prompting. These strategies provide additional flexibility to scale test-time computation (e.g., by increasing retrieved documents or generation steps), thereby enhancing LLMs' ability to effectively acquire and utilize contextual information. We address two key questions: (1) How does RAG performance benefit from the scaling of inference computation when optimally configured? (2) Can we predict the optimal test-time compute allocation for a given budget by modeling the relationship between RAG performance and inference parameters? Our observations reveal that increasing inference computation leads to nearly linear gains in RAG performance when optimally allocated, a relationship we describe as the inference scaling laws for RAG. Building on this, we further develop the computation allocation model to estimate RAG performance across different inference configurations. The model predicts optimal inference parameters under various computation constraints, which align closely with the experimental results. By applying these optimal configurations, we demonstrate that scaling inference compute on long-context LLMs achieves up to 58.9% gains on benchmark datasets compared to standard RAG.

• The paper introduces DRAG and IterDRAG, key strategies that enhance LLM performance by integrating extensive context and iterative query decomposition.
• It establishes inference scaling laws that demonstrate a linear performance increase with optimal context length and computation configuration.
• The work presents a computation allocation model that significantly improves results, with IterDRAG achieving up to 58.9% gains on benchmark datasets.

Inference Scaling for Long-Context Retrieval Augmented Generation

The paper "Inference Scaling for Long-Context Retrieval Augmented Generation" addresses critical aspects of enhancing performance in long-context LLMs with a focus on retrieval augmented generation (RAG). By investigating inference scaling strategies, the authors aim to improve the ability of LLMs to effectively handle knowledge-intensive tasks that involve processing extensive contextual information.

Core Contributions

The study emphasizes two primary inference scaling strategies: demonstration-based RAG (DRAG) and iterative demonstration-based RAG (IterDRAG). These approaches provide flexibility in scaling computation at test time, allowing for the optimization of RAG performance by increasing the quantity of retrieved documents and in-context examples, as well as introducing multiple generations steps. The paper presents an analysis of the impacts of these strategies across various benchmark datasets, modeling the relationships between RAG performance and the configuration of inference parameters.

Key contributions include:

• Demonstration-Based RAG: DRAG utilizes the long-context capabilities of LLMs by incorporating both extensive documents and in-context examples in the input. This design allows the LLM to better utilize the expanded context for generating answers in a single inference step.
• Iterative Demonstration-Based RAG: IterDRAG decomposes complex queries into simpler sub-queries and iteratively retrieves additional context, enhancing LLMs' knowledge retrieval and reasoning capabilities. This iterative approach leverages multiple inference steps to improve upon compositional reasoning tasks that DRAG alone may not address optimally.
• Inference Scaling Laws: The authors propose the inference scaling laws for RAG, demonstrating that performance scales linearly with increased effective context length under optimal configurations of inference parameters. This finding suggests a systematic method to predict and enhance LLMs' performance by scaling computational resources effectively.
• Computation Allocation Model: The paper introduces a computation allocation model that estimates optimal inference parameters for various computation constraints, guiding the effective allocation of resources to maximize RAG performance.

Experimental Outcomes

Empirical results reveal that the proposed strategies, DRAG and IterDRAG, substantially outperform traditional approaches, such as zero-shot and many-shot question answering, particularly when handling large-scale queries. IterDRAG, in particular, showcases superior performance for extensive contexts, achieving up to 58.9\% gains on benchmark datasets when the computation budget is expanded to five million tokens.

Implications and Future Directions

The findings of this paper have significant theoretical and practical implications. The identification of inference scaling laws offers a robust framework for understanding and predicting LLM behavior in long-context tasks. The computation allocation model provides actionable insights into resource allocation, optimizing test-time computation to achieve better results. Future work in this domain may explore more sophisticated retrieval methods, refining document relevance, and addressing intrinsic limitations in long-context LLMs' modeling capabilities. Additionally, the exploration of dynamic retrieval approaches could present opportunities for further enhancements in inference efficiency and accuracy.

This paper advances the state-of-the-art in retrieval augmented generation by offering coherent strategies and theoretical foundations for scaling inference capabilities in long-context LLM applications. Through systematic exploration of inference trade-offs, it sets the stage for more efficient and effective AI systems in knowledge-intensive domains.