R-Bot: An LLM-based Query Rewrite System

• URL: http://arxiv.org/abs/2412.01661v1
• Date: Mon, 02 Dec 2024 16:13:04 GMT
• Title: R-Bot: An LLM-based Query Rewrite System
• Authors: Zhaoyan Sun, Xuanhe Zhou, Guoliang Li,
• Abstract summary: We propose R-Bot, a query rewrite system based on machine learning.<n>We first design a multi-source rewrite evidence preparation pipeline to generate query rewrite evidences.<n>We then propose a hybrid-semantics retrieval method that combines structural and semantic analysis.<n>We conduct comprehensive experiments on widely used benchmarks, and demonstrate the superior performance of our system.
• Score: 15.46599915198438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query rewrite is essential for optimizing SQL queries to improve their execution efficiency without changing their results. Traditionally, this task has been tackled through heuristic and learning-based methods, each with its limitations in terms of inferior quality and low robustness. Recent advancements in LLMs offer a new paradigm by leveraging their superior natural language and code comprehension abilities. Despite their potential, directly applying LLMs like GPT-4 has faced challenges due to problems such as hallucinations, where the model might generate inaccurate or irrelevant results. To address this, we propose R-Bot, an LLM-based query rewrite system with a systematic approach. We first design a multi-source rewrite evidence preparation pipeline to generate query rewrite evidences for guiding LLMs to avoid hallucinations. We then propose a hybrid structure-semantics retrieval method that combines structural and semantic analysis to retrieve the most relevant rewrite evidences for effectively answering an online query. We next propose a step-by-step LLM rewrite method that iteratively leverages the retrieved evidences to select and arrange rewrite rules with self-reflection. We conduct comprehensive experiments on widely used benchmarks, and demonstrate the superior performance of our system, R-Bot, surpassing state-of-the-art query rewrite methods.

Related papers

• QUITE: A Query Rewrite System Beyond Rules with LLM Agents [16.501023983083083]
  Existing approaches mainly rely on predefined rewrite rules, but they handle a limited subset of queries and can cause performance regressions.<n>We propose QUITE ( query rewrite), a training-free and feedback-aware system based on Large Language Models (LLMs)<n>Extensive experiments show that QUITE reduces query execution time by up to 35.8% over state-of-the-art approaches and produces 24.1% more rewrites than prior methods.
  arXiv  Detail & Related papers  (2025-06-09T11:51:27Z)
• RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning [22.495874056980824]
  We propose Representation learning and textbfReasoning empowered retrieval-textbfAugmented textbfLarge textbfLanguage model textbfRecommendation (RALLRec+).
  arXiv  Detail & Related papers  (2025-03-26T11:03:34Z)
• RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems [19.674493253615235]
  We propose a novel hybrid pipeline for QR that balances efficiency and effectiveness.<n>Our approach combines offline knowledge distillation to create a lightweight but efficient student model with online reinforcement learning (RL) to refine query rewriting dynamically using real-time feedback.<n> Experimental results on Amazon ESCI dataset demonstrate significant improvements in query relevance, diversity, and adaptability.
  arXiv  Detail & Related papers  (2025-01-29T23:41:12Z)
• Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs [29.735465300269993]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they often struggle with spatial reasoning.<n>This paper presents a novel neural-symbolic framework that enhances LLMs' spatial reasoning abilities through iterative feedback between LLMs and Answer Set Programming (ASP)<n>We evaluate our approach on two benchmark datasets: StepGame and SparQA.
  arXiv  Detail & Related papers  (2024-11-27T18:04:05Z)
• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• FIRST: Faster Improved Listwise Reranking with Single Token Decoding [56.727761901751194]
  First, we introduce FIRST, a novel listwise LLM reranking approach leveraging the output logits of the first generated identifier to directly obtain a ranked ordering of the candidates. Empirical results demonstrate that FIRST accelerates inference by 50% while maintaining a robust ranking performance with gains across the BEIR benchmark. Our results show that LLM rerankers can provide a stronger distillation signal compared to cross-encoders, yielding substantial improvements in retriever recall after relevance feedback.
  arXiv  Detail & Related papers  (2024-06-21T21:27:50Z)
• RaFe: Ranking Feedback Improves Query Rewriting for RAG [83.24385658573198]
  We propose a framework for training query rewriting models free of annotations. By leveraging a publicly available reranker, oursprovides feedback aligned well with the rewriting objectives.
  arXiv  Detail & Related papers  (2024-05-23T11:00:19Z)
• LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency [65.01402723259098]
  We propose a novel method of query rewrite named LLM-R2, adopting a large language model (LLM) to propose possible rewrite rules for a database rewrite system. Experimental results have shown that our method can significantly improve the query execution efficiency and outperform the baseline methods.
  arXiv  Detail & Related papers  (2024-04-19T13:17:07Z)
• LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
  Recent large language models (LLM) are leveraging human feedback to improve their generation quality. We propose LLMRefine, an inference time optimization method to refine LLM's output. We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization. LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
  arXiv  Detail & Related papers  (2023-11-15T19:52:11Z)
• Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting [42.35788605017555]
  We propose utilizing large language models (LLMs) as query rewriters. We define four essential properties for well-formed rewrites and incorporate all of them into the instruction. We introduce the role of rewrite editors for LLMs when initial query rewrites are available, forming a "rewrite-then-edit" process.
  arXiv  Detail & Related papers  (2023-10-15T03:04:17Z)
• Context Aware Query Rewriting for Text Rankers using LLM [5.164642900490078]
  We analyze the utility of large-language models for improved query rewriting for text ranking tasks. We adopt a simple, yet surprisingly effective, approach called context aware query rewriting (CAR) We find that fine-tuning a ranker using re-written queries offers a significant improvement of up to 33% on the passage ranking task and up to 28% on the document ranking task.
  arXiv  Detail & Related papers  (2023-08-31T14:19:50Z)
• Allies: Prompting Large Language Model with Beam Search [107.38790111856761]
  In this work, we propose a novel method called ALLIES. Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query. By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly through retrieval.
  arXiv  Detail & Related papers  (2023-05-24T06:16:44Z)
• Query Rewriting for Retrieval-Augmented Large Language Models [139.242907155883]
  Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs.
  arXiv  Detail & Related papers  (2023-05-23T17:27:50Z)
• Large Language Models are Strong Zero-Shot Retriever [89.16756291653371]
  We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM.
  arXiv  Detail & Related papers  (2023-04-27T14:45:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.