The Unreasonable Effectiveness of LLMs for Query Optimization
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.
Code (1)
Similar Papers 제목 키워드 기반
The unreasonable effectiveness of pattern matching
We report on an astonishing ability of large language models (LLMs) to make sense of "Jabberwocky" language in which most or all content words have been randomly replaced by nonsense strings, e.g., translating "He dwushe…
Large Language Models Are Struggle to Cope with Unreasonability in Math Problems
Recent research have demonstrated LLMs' impressive performance in math and reasoning. However, the capacity of LLMs to address math problems under unconventional conditions, such as internal inconsistencies and flawed as…
MathThe Unreasonable Effectiveness of Word Representations for Twitter Named Entity Recognition
Comments on Sejnowski's "The unreasonable effectiveness of deep learning in artificial intelligence" [arXiv:2002.04806]
Terry Sejnowski's 2020 paper [arXiv:2002.04806] is entitled "The unreasonable effectiveness of deep learning in artificial intelligence". However, the paper doesn't attempt to answer the implied question of why Deep Conv…
A Survey of Query Optimization in Large Language Models
\textit{Query Optimization} (QO) refers to techniques aimed at enhancing the efficiency and quality of Large Language Models (LLMs) in understanding and answering queries, especially complex ones in scenarios like Retrie…
RAGRetrievalRetrieval-augmented GenerationSurvey