We introduce SEED, an LLM-centric system that allows users to easily create
efficient, and effective data management applications. SEED comprises three
main components: code generation, model generation, and augmented LLM query to
address the challenges that LLM services are computationally and economically
expensive and do not always work well on all cases for a given data management
task. SEED addresses the expense challenge by localizing LLM computation as
much as possible. This includes replacing most of LLM calls with local code,
local models, and augmenting LLM queries with batching and data access tools,
etc. To ensure effectiveness, SEED features a bunch of optimization techniques
to enhance the localized solution and the LLM queries, including automatic code
validation, code ensemble, model representatives selection, selective tool
usages, etc. Moreover, with SEED users are able to easily construct a data
management solution customized to their applications. It allows the users to
configure each component and compose an execution pipeline in natural language.
SEED then automatically compiles it into an executable program. We showcase the
efficiency and effectiveness of SEED using diverse data management tasks such
as data imputation, NL2SQL translation, etc., achieving state-of-the-art
few-shot performance while significantly reducing the number of required LLM
calls.

SEED 是一种面向 LLM 的系统，允许用户轻松创建高效和有效的数据管理应用程序，通过提供代码生成、模型生成和增强 LLM 查询等主要组件来解决 LLM 服务计算和经济上的挑战，并展示其在不同数据管理任务中的效率和有效性。