This Google DeepMind AI article explores inference scaling in long-context RAG

long context Large Language Models (LLM) They are designed to handle long sequences of input, allowing them to process and understand large amounts of information. As interference computing power increases, large language models (LLMs) can perform various tasks. Particularly for knowledge-intensive tasks that rely primarily on Recovery Augmented Generation (RAG)Increasing the number or size of recovered documents up to a certain level consistently increases performance. For knowledge-intensive tasks, increased computation is often allocated to incorporate more external knowledge. However, simply adding more information does not always improve performance. Numerous studies have also shown that reading more information can also add noise and therefore can even cause performance degradation. As a result, long-context RAG inference scaling remains a challenge for existing methods.

Early work on extending context length involves techniques such as sparse/low-rank cores to reduce memory requirements. In addition to this, recurrent and state-space models (SSM) are proposed as efficient substitutes for transformer-based models. Recent advances in efficient attention methods allow LLMs to train and infer input sequences comprising millions of tokens. Learning in context (ICL) is a way to make models more efficient by showing them some examples of the task during inference (when processing answers). To further improve the performance of ICL, existing works focus on pre-training methods that optimize language models for understanding and learning in context. With the emergence of long-context LLMs, it is possible to expand the number of examples in ICL. Recovery Augmented Generation (RAG) improves language model performance by using useful information from external sources. Instead of simply using random information or data, improving the way the model selects relevant documents helps it generate better answers and better predictions. Additionally, coding documents can increase knowledge retrieval and generate more accurate information. Recently, methods have been proposed to handle large and lengthy documents and expand the storage of retrieved data to further improve the performance of RAG.

Despite such progress, scaling inference has not yet been sufficiently explored for long-context RAG methods in knowledge-intensive settings. To close this gap, the researchers investigated how variations in inference computation affect RAG performance, with the intention of optimizing computation allocation at test time in subsequent tasks.

A group of researchers from Google Deep Mind, the University of Illinois, Urbana-Champaign and the University of Massachusetts Amherst studied the inference scale for Recovery Augmented Generation (RAG)exploring methods that go beyond simply increasing the amount of data. They mainly focused on two inference escalation strategies: learning in context and iterative prompts. These strategies provide additional flexibility to scale the testing time calculation, thereby improving LLMs' ability to effectively acquire and use context-related information. Research observations revealed that increasing inference computation leads to nearly linear gains in RAG performance when allocated optimally, a relationship described as the inference scaling laws for RAG. On this basis, they further developed the computational mapping model to estimate the performance of RAG under different inference settings. The model predicts optimal inference parameters under various computational constraints, which closely align with experimental results. The researchers used a simple approach by introducing Demo-based RAG (DRAG)where multiple examples are taken to teach the model how to find and apply relevant information. While DRAG helps, single retrieval often does not provide enough information for more complex tasks. To solve this, they developed iterative DRAG (IterDRAG), which breaks queries into smaller parts, retrieves information in steps, and generates answers by reasoning through these smaller queries, helping models handle more complex tasks. In iteradraggingYou can also expand the number of steps the model follows to generate a response. Experiments showed that by increasing the amount of computation used, both DRAG and IterDRAG consistently improved their performance, and IterDRAG performed even better when retrieving and generating in steps. This shows an almost linear improvement in RAG performance as we increase computing power, especially when the correct configurations are used. This iterative process helps handle more difficult tasks by focusing on each subpart of the query. Both methods scale the inference computation, improving performance by making better use of context and retrieved knowledge.

The researcher evaluated the performance of different recovery-augmented generation (RAG) strategies on various computational budgets. In comparison, DRAG and IterDRAG were found to exhibit superior scalability compared to the QA and RAG baselines, with DRAG excelling at shorter context lengths (up to 32k tokens) and IterDRAG works better with longer contexts (up to 5M tokens). DRAG performance continues to improve until 1M tokens, while IterDRAG benefits from iterative recovery and generation with even larger budgets. Observations revealed that increasing inference computation leads to nearly linear gains in RAG performance when allocated optimally, a relationship we describe as the inference scaling laws for RAG. The model predicts optimal inference parameters under various computational constraints, which closely align with experimental results. By applying the optimal settings, they demonstrate that scale inference computed in long-context LLM achieves up to 58.9% profits on reference data sets compared to the standard RAG.

In conclusion, the researchers designed the introduction of two innovative strategies, DRAG and IterDRAG, to improve the computational efficiency of recovery augmented generation (RAG). Through experimental validation, they demonstrated that these strategies significantly outperform the traditional approach of simply increasing the number of documents retrieved. From the observations, they derived inference scaling laws for RAG and the corresponding computation allocation model, designed to predict the performance of RAG under different hyperparameters. Through extensive experiments, he demonstrated that optimal configurations can be accurately estimated and closely aligned with experimental results. These insights can provide a solid foundation for future research on optimizing inference strategies for long-context RAG.

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Divyesh is a Consulting Intern at Marktechpost. He is pursuing a BTech in Agricultural and Food Engineering from the Indian Institute of technology Kharagpur. He is a data science and machine learning enthusiast who wants to integrate these leading technologies in agriculture and solve challenges.