Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely (2409.14924v1)

Abstract: LLMs augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LLMs, such as Retrieval-Augmented Generation (RAG) and fine-tuning, are gaining increasing attention and widespread application. Nonetheless, the effective deployment of data-augmented LLMs across various specialized fields presents substantial challenges. These challenges encompass a wide range of issues, from retrieving relevant data and accurately interpreting user intent to fully harnessing the reasoning capabilities of LLMs for complex tasks. We believe that there is no one-size-fits-all solution for data-augmented LLM applications. In practice, underperformance often arises from a failure to correctly identify the core focus of a task or because the task inherently requires a blend of multiple capabilities that must be disentangled for better resolution. In this survey, we propose a RAG task categorization method, classifying user queries into four levels based on the type of external data required and primary focus of the task: explicit fact queries, implicit fact queries, interpretable rationale queries, and hidden rationale queries. We define these levels of queries, provide relevant datasets, and summarize the key challenges and most effective techniques for addressing these challenges. Finally, we discuss three main forms of integrating external data into LLMs: context, small model, and fine-tuning, highlighting their respective strengths, limitations, and the types of problems they are suited to solve. This work aims to help readers thoroughly understand and decompose the data requirements and key bottlenecks in building LLM applications, offering solutions to the different challenges and serving as a guide to systematically developing such applications.

Comprehensive Survey on Retrieval Augmented Generation (RAG) and Techniques

This paper, authored by Siyun Zhao et al., provides a comprehensive survey of the methods to augment LLMs with external data, with a significant emphasis on Retrieval-Augmented Generation (RAG) and related techniques. By focusing on various challenges and effective integration strategies across different specializations, the authors deliver a well-defined categorization of tasks and queries, addressing their distinctive requirements and proposing systematic solutions.

Overview of External Data Integration Techniques

The paper begins with an introduction highlighting the significant leaps LLMs have made in terms of world knowledge and reasoning abilities. However, it points out the existing limitations, such as model hallucinations and the necessity for domain-specific knowledge. Incorporating external data through methods like RAG and fine-tuning is proposed to fill these gaps, enabling models to perform more accurately across specialized fields.

Categorization of Queries

A novel contribution of this survey is the categorization of user queries into four distinct levels based on the required type of external data and task complexity:

1. Explicit Fact Queries: These involve the straightforward retrieval of information based on explicit facts present in the external data.
2. Implicit Fact Queries: These require combining multiple pieces of information, necessitating common sense reasoning or basic logic for aggregation.
3. Interpretable Rationale Queries: These queries require understanding and applying explicit, domain-specific rationales and reasoning steps from external data.
4. Hidden Rationale Queries: These represent the most complex category, demanding inference from implicit, dispersed knowledge and abstract patterns.

Detailed Analysis of Query Levels

Explicit Fact Queries

These queries are primarily addressed through RAG, involving steps like data processing, data retrieval, and response generation. Specific challenges include effectively parsing multi-modal documents, optimizing text chunking strategies, and enhancing retrieval mechanisms to filter and rank the most relevant data sections. Techniques such as index creation (sparse, dense, and hybrid), iterative retrieval, and re-ranking strategies are discussed to improve accuracy and efficiency.

Implicit Fact Queries

This category requires sophisticated methods to aggregate multiple pieces of retrieved data. The survey explores advanced iterative RAG techniques and the employment of graph and tree representations to elucidate logical connections between dispersed data segments. Additionally, methodologies such as natural language to SQL queries facilitate structured data analysis, enhancing the model's ability to handle aggregated queries.

Interpretable Rationale Queries

To address these queries, the paper emphasizes incorporating domain-specific rationales into LLMs via prompt tuning and CoT prompting techniques. For instance, using reinforcement learning to optimize prompts or designing manual prompts that align with specialized workflows, like medical guidelines or customer service protocols, is discussed. Additionally, leveraging agent-based systems that encapsulate domain knowledge within workflow structures further enhances the application of LLMs in dynamic, real-world tasks.

Hidden Rationale Queries

These are tackled through methods that infer latent rationales and expertise from substantial datasets. Offline learning, where guidelines and principles are extracted from historical data, serves as one approach. In-context learning, leveraging examples and chains of thought, enables dynamic adaptation of LLMs to new problem-solving patterns. Fine-tuning, despite its computational demands, is another robust method, allowing LLMs to internalize extensive domain-specific rationales effectively.

Implications for Future Research and Application

The survey underlines that understanding the nature of queries and aligning them with appropriate techniques is critical for developing effective data-augmented LLM applications. Moreover, the paper highlights the importance of selecting suitable methods for injecting knowledge into LLMs, weighing factors like data volume, computational resources, and training specifics.

By categorizing queries and detailing nuanced approaches for each level, this work serves as a comprehensive guide for researchers and practitioners in the field of LLM development. Future advancements are likely to center around optimizing these methods further, enhancing the adaptability, efficiency, and accuracy of data-augmented LLM applications across diverse domains.

The paper makes a significant contribution to the discourse on LLM augmentation techniques, providing a structured approach to understanding and addressing the multi-faceted challenges these techniques entail. Researchers in the field can leverage the insights and methodologies discussed to refine their applications, driving forward the capabilities of LLMs in practical, domain-specific scenarios.