Retrieval-Augmented Generation (RAG) is essential for integrating external knowledge into Large Language Model (LLM) outputs. While the literature on RAG is growing, it primarily focuses on systematic reviews and comparisons of new state-of-the-art (SoTA) techniques against their predecessors, with a gap in extensive experimental comparisons. This study begins to address this gap by assessing various RAG methods' impacts on retrieval precision and answer similarity. We found that Hypothetical Document Embedding (HyDE) and LLM reranking significantly enhance retrieval precision. However, Maximal Marginal Relevance (MMR) and Cohere rerank did not exhibit notable advantages over a baseline Naive RAG system, and Multi-query approaches underperformed. Sentence Window Retrieval emerged as the most effective for retrieval precision, despite its variable performance on answer similarity. The study confirms the potential of the Document Summary Index as a competent retrieval approach. All resources related to this research are publicly accessible for further investigation through our GitHub repository ARAGOG (https://github.com/predlico/ARAGOG). We welcome the community to further this exploratory study in RAG systems.
This paper examines various Retrieval-Augmented Generation (RAG) techniques, evaluating their impact on enhancing Large Language Model (LLM) outputs through dynamic external knowledge integration.
It categorizes RAG techniques into groups aimed at optimizing retrieval precision and answer generation, conducting a detailed comparative analysis using specific metrics.
Utilizing a tailored dataset from the AI arXiv collection and the GPT-3.5-turbo model, the study explores the effectiveness of different RAG methods in improving retrieval precision and mitigating LLM output variability.
The findings indicate significant performance differences among RAG techniques, with Sentence-window Retrieval and HyDE standing out, suggesting varied efficacy in enhancing LLM outputs and highlighting directions for future research.
The realm of NLP has been revolutionized by the advent of LLMs, which have shown immense potential in generating text and answering queries. Despite their capabilities, one key challenge that persists is the integration of dynamic external knowledge to enhance these models' outputs. Retrieval-Augmented Generation (RAG) systems have emerged as a solution, embedding external knowledge into LLM outputs to yield more informed and context-aware responses. This research paper evaluates various RAG techniques, offering insights into their effectiveness through a detailed experimental comparison.
The study categorizes the evaluated RAG techniques into distinct groups, focusing on their intent to optimize retrieval precision and answer generation. Techniques such as Sentence-window retrieval and Document summary index aim to decouple retrieval from generation to improve overall performance. On the other hand, Query Expansion methods like HyDE and Multi-query expand upon the initial query in unique ways to enhance document retrieval. Re-rankers, including the Cohere Reranker and LLM-based Reranker, refine the selection of documents post-retrieval to ensure only the most relevant information aids the generation process. The study meticulously evaluates these techniques using metrics like Retrieval Precision and Answer Similarity.
Adopting a robust experimental setup, this study harnesses a tailored dataset derived from the AI arXiv collection, comprising 423 AI and LLM-related papers. The dataset serves dual purposes: constructing a comprehensive database for RAG system evaluation and generating a set of evaluation data to assess the effectiveness of RAG methods. Leveraging the GPT-3.5-turbo model, the study employed a strategic selection of RAG techniques aimed at enhancing retrieval precision, exploring a comparative analysis with an emphasis on mitigating LLM output variability.
The findings reveal significant differences in the performance of RAG techniques with respect to retrieval precision. Specifically, Sentence Window Retrieval stands out for its effectiveness, although it does not consistently correlate with higher Answer Similarity scores. HyDE and LLM reranking significantly improve retrieval precision, surpassing baseline Naive RAG. Conversely, Maximal Marginal Relevance and Cohere Rerank do not exhibit marked advantages, and Multi-query approaches underperform compared to the baseline. Through comprehensive statistical analysis, these results underscore the varied efficacy of RAG techniques in enhancing LLM outputs.
The study acknowledges its limitations, such as the exclusive use of the GPT-3.5-turbo model for evaluation and the reliance on a singular dataset. The inherent variability introduced by different chunking strategies is also noted, highlighting the difficulty in directly comparing the performance of various retrieval methods. Looking ahead, the paper identifies several promising avenues for future research, including the exploration of Knowledge Graph RAG systems and the concept of 'Unfrozen' RAG systems that adapt dynamically to specific datasets. Furthermore, the potential for Auto-RAG, analogous to Auto-ML in machine learning, offers an exciting frontier for automating the optimization of RAG system configurations.
This study fills a significant gap in the literature by providing an extensive experimental comparison of advanced RAG techniques. By leveraging a tailored dataset and employing robust metrics like Retrieval Precision and Answer Similarity, the research unfolds nuanced insights into the efficacy of these techniques. The findings not only contribute to a deeper understanding of RAG systems but also pave the way for future inquiries into enhancing LLMs' performance through dynamic knowledge integration. Through evaluation, limitation acknowledgment, and future direction suggestions, the paper acts as a foundational resource for ongoing exploration in the domain of Retrieval-Augmented Generation systems.
Akash. Hybrid search: Optimizing rag implementation. https://medium.com/@csakash03/hybrid-search-is-a-method-to-optimize-rag-implementation-98d9d0911341, 2023. Accessed: 2024-04-01.
T. Bratanic. Using a knowledge graph to implement a rag application. https://neo4j.com/developer-blog/knowledge-graph-rag-application/, 2023. Accessed: 2024-03-24.
J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf, 1998. Accessed: 2024-03-24.
James Calam. Ai arxiv dataset. https://huggingface.co/datasets/jamescalam/ai-arxiv, 2023. Accessed: 2024-03-24.
D. Kiela. Stanford cs25: V3 i retrieval augmented language models. https://www.youtube.com/watch?v=mE7IDf2SmJg, 2024. Accessed: 2024-03-24.
Langchain. Query transformations. https://blog.langchain.dev/query-transformations/, 2023. Accessed: 2024-03-23.
J. Liu. A new document summary index for llm-powered qa systems. https://www.llamaindex.ai/blog/a-new-document-summary-index-for-llm-powered-qa-systems-9a32ece2f9ec, 2023a. Accessed: 2024-03-23.
J. Liu. Using llms for retrieval and reranking. https://www.llamaindex.ai/blog/using-llms-for-retrieval-and-reranking-23cf2d3a14b6, 2023b. Accessed: 2024-03-24.
Markr.AI. Autorag: A framework for automated retrieval-augmented generation. https://github.com/Marker-Inc-Korea/AutoRAG, 2024. Accessed: 2024-03-24.
K. Phaneendra. Deep dive into advanced rag applications in llm-based systems. https://phaneendrakn.medium.com/deep-dive-into-advanced-rag-applications-in-llm-based-systems-1ccee0473b3b, 2023. Accessed: 2024-04-01.
Pinecone. Rerankers. https://www.pinecone.io/learn/series/rag/rerankers/, 2023. Accessed: 2024-03-24.
Predlico. Aragog - advanced retrieval augmented generation output grading. https://github.com/predlico/ARAGOG, 2024. Accessed: 2024-03-24.
RAGAS Documentation. Metrics. https://docs.ragas.io/en/v0.0.17/concepts/metrics/index.html, 2023. Accessed: 2024-03-24.
Tonic AI. About rag metrics: Tonic validate rag metrics summary. https://docs.tonic.ai/validate/about-rag-metrics/tonic-validate-rag-metrics-summary, 2023. Accessed: 2024-03-24.
S. Yang. Advanced rag 01: Small to big retrieval. https://towardsdatascience.com/advanced-rag-01-small-to-big-retrieval-172181b396d4, 2023. Accessed: 2024-03-23.