Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025)

Abstract: We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge requirements of Large Language Models (LLMs). Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting parametric knowledge from LLMs, followed by using a specialized query optimizer for refining these queries. This process ensures the retrieval of only the most pertinent information essential for generating accurate responses. Moreover, to enhance flexibility and reduce computational costs, we propose a trainable scheme for our pipeline that utilizes a smaller, tunable model as the query optimizer, which is refined through knowledge distillation from a larger teacher model. Our evaluations on various question-answering (QA) datasets and with different retrieval systems show that ERRR consistently outperforms existing baselines, proving to be a versatile and cost-effective module for improving the utility and accuracy of RAG systems.

## introduction the field of natural language processing (nlp) has witnessed transformative advancements in recent years, largely driven by the advent of large language models (llms). these models, trained on vast corpora, have demonstrated exceptional capabilities in understanding human text and generating high-quality responses @xcite . they have also proven practical and scalable for various downstream nlp tasks, such as conversational response generation, text summarization, and content recommendation, even in few-shot or zero-shot settings @xcite . despite their strengths, a key limitation of llms lies in their reliance on static training data, which causes them to struggle with dynamic or less commonly known information outside their initial training scope. this limitation often leads to outdated, inaccurate, or entirely fabricated responses-a phenomenon commonly referred to as "hallucination" @xcite . to address this issue, retrieval-augmented generation (rag) @xcite has emerged as a promising approach to enhance the functionality and reliability of llms. by integrating external knowledge sources through retrieval systems, rag enables llms to augment user queries with relevant, up-to-date information. this augmentation allows llms to generate more contextually accurate and relevant responses. for instance, in a conversational setting where a user queries an llm like chatgpt @xcite for the latest news, rag retrieves pertinent articles to supplement the static pre-trained knowledge of the model, thereby mitigating the information gap. while retrieval augmentation has proven effective in mitigating hallucinations, it introduces its own set of challenges. a prominent issue in retrieval-augmented generation (rag) systems is the pre-retrieval gap-a mismatch between the information retrieved using the original user query and the specific knowledge required to generate optimal responses @xcite . for instance, consider a document collection containing three passages, labeled passage a, b, and c, each containing unique knowledge components x, y, and z, respectively. although all three passages include keywords associated with knowledge z-the user's intended target-a poorly formulated query may lead to retrieving passage a or b instead of the ideal passage c. this misalignment restricts the llm reader's ability to generate accurate responses, making the pre-retrieval gap a critical barrier to achieving optimal text generation in rag systems. to bridge the pre-retrieval gap, the rewrite-retrieve-read (rrr) framework @xcite introduced query rewriting as a mechanism to optimize user queries and improve their alignment with retrieval systems. however, rrr and similar methods @xcite primarily focus on rephrasing or broadening queries, which helps expand the search scope but fails to address the specific knowledge requirements of the llm reader. additionally, recent self-prompting techniques @xcite have explored using chain-of-thought (cot) prompts and pseudo-qa pairs to enhance llm reasoning capabilities by eliciting internal parametric knowledge. while these approaches effectively improve the internal reasoning and explanation capabilities of llms for tasks like multi-hop reasoning and opendomain qa, they lack mechanisms for aligning external retrieval queries with the llm's knowledge gaps, making them insufficient for resolving the pre-retrieval gap in retrieval-augmented generation (rag) systems. to this end, we propose extract-refine-retrieve-read (errr), a straightforward yet effective framework designed for retrieval augmentation systems. the errr framework is crafted to bridge the pre-retrieval information gap through tailored query optimization and aims to resolve the inherent limitations of rrr by enabling retrieval based on the specific information needs of the llm reader. specifically, it initiates by extracting parametric knowledge from llms and employs a specialized query optimizer that refines user queries. this refinement either complements or validates the extracted parametric knowledge, ensuring that only essential information is retrieved for generating accurate responses, and minimizing the retrieval of extraneous information that could degrade output quality. in addition to its innovative query optimization process, errr introduces a trainable scheme to enhance efficiency and adaptability. recognizing the constraints posed by black-box systems like chat-gpt @xcite , which are accessible only through inference apis, errr incorporates a smaller, tunable language model as the query optimizer. this trainable component reduces computational costs while offering greater flexibility to customize the retrieval process for diverse queries and knowledge sources. by combining precision in addressing pre-retrieval gaps with cost-effective adaptability, errr provides a robust solution for improving retrieval augmentation in llm-driven systems. we evaluate errr on multiple questionanswering (qa) datasets, including hotpotqa @xcite , ambignq @xcite , and popqa @xcite , using both web-based (e.g., brave search engine) and local retrieval systems (e.g., dense passage retrieval @xcite ). across all tested datasets and retrieval configurations, errr consistently outperforms baseline frameworks, such as rrr, in terms of retrieval accuracy and response quality. these results highlight errr's versatility and effectiveness in diverse settings. in summary, our key contributions are as follows: (i) we propose extract-refine-retrieve-read (errr), a novel framework that optimizes queries to bridge the pre-retrieval gap and enhance rag systems. (ii) we demonstrate errr's adaptability across different datasets, retrieval systems, and settings, establishing its robustness and versatility. (iii) we introduce a trainable errr scheme that reduces computational costs while maintaining high performance, making it suitable for real-world applications. ## methodology in this section, we elaborate on the details of extract-refine-retrieve-read (errr), a framework for improving retrieval-augmented llms through query optimization for parametric knowledge refinement. section 3.1 formally defines the central task addressed by errr and introduces its key concepts. the design of the framework is discussed in section 3.2, where we outline a frozen scheme using a black-box llm reader and standard web search tools. additionally, section 3.3 discusses a trainable scheme of the framework. ## conclusion in this paper, we present extract-refine-retrieve-read (errr) framework for retrieval-augmented generation (rag) systems. the errr framework is designed to optimize queries, aligning them closely with the specific informational needs of large language models (llms) to enhance retrieval augmentation effectiveness. our experimental results demonstrate that our method surpasses both the naive llm and native query rewriting framework rewrite-retrieve-read on benchmark datasets such as ambigqa @xcite , popqa @xcite and hotpotqa @xcite , utilizing both web search tools and a dense retriever with local static corpus. these results demonstrated errr's remarkable adaptability across a variety of settings, data sources, and retrieval systems. this flexibility ensures that errr can be effectively implemented in diverse operational environments, making it a potential and adaptable component for inclusion in more advanced rag systems. additionally, we have developed and implemented a trainable scheme for the errr framework. this approach is both cost-effective and efficient as it relies on only a fine-tuned t5 model trained on a moderately sized dataset and surpasses the performance of the frozen gpt-3.5-turbo.