Generative Information Retrieval: RAG and GAR
Jimmy X. Huang
Information Retrieval and Knowledge Management Research Lab
York University, Toronto, CanadaAbstract. Large language models (LLMs) are revolutionizing information retrieval (IR). Retrieval-Augmented Generation (RAG) and GenerationAugmented Retrieval (GAR) leverage LLMs to enhance IR. RAG improves response accuracy by integrating verified external information,
addressing the “hallucinated” content. GAR refines search queries andenhances document representations, leading to more relevant results. Theinteraction between RAG and GAR creates a powerful synergy, improving both retrieval and generation processes. It is expected that roughset theory can be utilized to optimize the process in RAG and GAR via
identifying the key features.
Keywords: Generative Information Retrieval · Generation-augmented
Retrieval · Retrieval-augmented Generation.

1

Introduction

The emergence of large language models (LLMs) has led to significant advancements in information retrieval (IR) technologies, resulting in the developmentof methods such as Retrieval-Augmented Generation (RAG) and GenerationAugmented Retrieval (GAR) [4]. These methods utilize the advanced generativeabilities and deep semantic understanding of LLMs to increase the precision andeffectiveness of information systems.
Retrieval-Augmented Generation (RAG) aims to improve the reliability ofresponses generated by LLMs [5]. By dynamically retrieving and integrating external information during the inference process, RAG attempts to anchor themodel’s responses in verified contents. This approach addresses the issue of “hallucinated” information—coherent yet factually incorrect content produced byLLMs. The success of RAG depends on the model’s ability to effectively use
the retrieved information, which relies on the quality and completeness of theexternal sources.
Conversely, Generation-Augmented Retrieval (GAR) seeks to improve search
results by utilizing the generative capabilities of LLMs. GAR employs thesemodels to extend and refine search queries or to enhance document representations [2][6], thus better aligning user queries with the document corpus. Thismethod not only increases the relevance of search results but also broadens therange of contents accessible in response to complex queries.
Rough sets have been successfully applied for Web mining (e.g. Web usage
mining and web page classification) [1][3]. We expect that rough set theory can2

Jimmy X. Huang

also be utilized to optimize the process in RAG and GAR, ensuring that theretrieved contents are highly relevant and specific to the user’s needs. This canbe achieved by identifying the key features of the information that are mostrelevant to the generation task.

2RAG and GAR

The interaction between RAG and GAR creates a powerful synergy where eachcomponent can inform and improve the other, forming a feedback loop that
potentially enhances both retrieval and generation processes.
From RAG to GAR: The factual content retrieved and validated by RAG
provides a reliable foundation for GAR tasks. By ensuring the accuracy of theinformation used to enhance queries or documents, RAG supports GAR in producing more focused and contextually relevant search enhancements. This leads
to more effective and precise information retrieval, minimizing the occurrence ofirrelevant results.
From GAR to RAG: In turn, the improved querying capabilities developed inGAR can substantially benefit RAG. Enhanced queries facilitate more effective
and accurate information retrieval during the RAG process, ensuring that theexternal content integrated during generation is highly relevant and specific tothe user’s needs. This not only enhances the quality of the generated responses
but also reduces the system’s load by preventing the retrieval of unnecessary
information.
The cyclical synergy between RAG and GAR presents a robust framework forleveraging the strengths of LLMs in both generating and retrieving information.
Continuously refining these interactions is crucial for developing next-generation
IR systems that are both intelligent and intuitive, capable of meeting the increasingly complex demands of users in the digital age. Further exploration andoptimization of the dynamics between RAG and GAR are essential for achieving
greater levels of accuracy and reliability in both generated content and search
results.

References
1. An, A., Huang, Y., Huang, X. and Cercone, N.: “Feature selection with rough sets
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2. Huang, J. X., Miao, J. and He, B.: “High performance query expansion using adaptive co-training”. IPM. 49(2):441-453, 2013.
3. Huang, X., Cercone, N. and An, A.: “Comparison of interestingness functions forlearning web usage patterns”. In Proc. of 2002 ACM CIKM, 617-620, 2002.
4. Huang, Y. and Huang, J. X.: “Exploring ChatGPT for next-generation information
retrieval: Opportunities and challenges”. Web Intelligence Journal. 1-16, 2024.
5. Huang, Y. and Huang, J. X.: “A Survey on Retrieval-Augmented Text Generationfor Large Language Models”. arXiv preprint arXiv:2404.10981. 2024.
6. Ye, Z., Huang, J. X. and Lin, H.: “Finding a good query-related topic for boosting
pseudo-relevance feedback”. JASIST. 62(4):748-760, 2011.