The Unreasonable Effectiveness of LLMs for Query Optimization

• URL: http://arxiv.org/abs/2411.02862v1
• Date: Tue, 05 Nov 2024 07:10:00 GMT
• Title: The Unreasonable Effectiveness of LLMs for Query Optimization
• Authors: Peter Akioyamen, Zixuan Yi, Ryan Marcus,
• Abstract summary: We show that embeddings of query text contain useful semantic information for query optimization. We show that a simple binary deciding between alternative query plans, trained on a small number of embedded query vectors, can outperform existing systems.
• Score: 4.50924404547119
• License:
• Abstract: Recent work in database query optimization has used complex machine learning strategies, such as customized reinforcement learning schemes. Surprisingly, we show that LLM embeddings of query text contain useful semantic information for query optimization. Specifically, we show that a simple binary classifier deciding between alternative query plans, trained only on a small number of labeled embedded query vectors, can outperform existing heuristic systems. Although we only present some preliminary results, an LLM-powered query optimizer could provide significant benefits, both in terms of performance and simplicity.

Related papers

• Data Fusion of Synthetic Query Variants With Generative Large Language Models [1.864807003137943]
  This work explores the feasibility of using synthetic query variants generated by instruction-tuned Large Language Models in data fusion experiments. We introduce a lightweight, unsupervised, and cost-efficient approach that exploits principled prompting and data fusion techniques. Our analysis shows that data fusion based on synthetic query variants is significantly better than baselines with single queries and also outperforms pseudo-relevance feedback methods.
  arXiv  Detail & Related papers  (2024-11-06T12:54:27Z)
• LLM-based Optimization of Compound AI Systems: A Survey [64.39860384538338]
  In a compound AI system, components such as an LLM call, a retriever, a code interpreter, or tools are interconnected. Recent advancements enable end-to-end optimization of these parameters using an LLM. This paper presents a survey of the principles and emerging trends in LLM-based optimization of compound AI systems.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars [66.823588073584]
  Large language models (LLMs) have shown impressive capabilities in real-world applications. The quality of these exemplars in the prompt greatly impacts performance. Existing methods fail to adequately account for the impact of exemplar ordering on the performance.
  arXiv  Detail & Related papers  (2024-05-25T08:23:05Z)
• MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation [10.726734105960924]
  Large language models (LLMs) have enabled in-context learning (ICL)-based methods that significantly outperform fine-tuning approaches for text-to- tasks. This study considers the sensitivity of LLMs to the prompts and introduces a novel approach that leverages multiple prompts to explore a broader search space for possible answers. We establish a new SOTA performance on the BIRD in terms of both the accuracy and efficiency of the generated queries.
  arXiv  Detail & Related papers  (2024-05-13T04:59:32Z)
• Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models [28.105271954633682]
  We introduce a query-dependent parameter efficient fine-tuning (Q-PEFT) approach for text reranking to leak information to Large Language Models (LLMs) We utilize the query to extract the top-$k$ tokens from input documents, serving as contextual clues. We further augment Q-PEFT by substituting the retrieval mechanism with a multi-head attention layer to achieve end-to-end training and cover all the tokens in the documents.
  arXiv  Detail & Related papers  (2024-04-06T06:44:41Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• No more optimization rules: LLM-enabled policy-based multi-modal query optimizer [9.370719876854228]
  Large language model (LLM) has marked a pivotal moment in the field of machine learning and deep learning. In this paper, we investigate the query optimization ability of LLM and use LLM to design LaPuda, a novel LLM and Policy based multi-modal query.
  arXiv  Detail & Related papers  (2024-03-20T13:44:30Z)
• Optimizing LLM Queries in Relational Workloads [58.254894049950366]
  We show how to optimize Large Language Models (LLMs) inference for analytical workloads that invoke LLMs within relational queries. We implement these optimizations in Apache Spark, with vLLM as the model serving backend. We achieve up to 4.4x improvement in end-to-end latency on a benchmark of diverse LLM-based queries on real datasets.
  arXiv  Detail & Related papers  (2024-03-09T07:01:44Z)
• Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL [62.824464372594576]
  We aim to enhance arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization. We introduce Prompt-OIRL, which harnesses offline inverse reinforcement learning to draw insights from offline prompting demonstration data.
  arXiv  Detail & Related papers  (2023-09-13T01:12:52Z)
• BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment [0.36700088931938835]
  BitE is a novel ensemble learning model using database statistics and metadata to tune a learned query for enhancing performance. Our model achieves 19.6% more improved queries and 15.8% less regressed queries compared to the existing traditional methods.
  arXiv  Detail & Related papers  (2023-06-01T16:05:33Z)

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.