Efficient processing of tabular data is important in various industries,
especially when working with datasets containing a large number of columns.
Large language models (LLMs) have demonstrated their ability on several tasks
through carefully crafted prompts. However, creating effective prompts for
tabular datasets is challenging due to the structured nature of the data and
the need to manage numerous columns. This paper presents an innovative
auto-prompt generation system suitable for multiple LLMs, with minimal
training. It proposes two novel methods; 1) A Reinforcement Learning-based
algorithm for identifying and sequencing task-relevant columns 2) Cell-level
similarity-based approach for enhancing few-shot example selection. Our
approach has been extensively tested across 66 datasets, demonstrating improved
performance in three downstream tasks: data imputation, error detection, and
entity matching using two distinct LLMs; Google flan-t5-xxl and Mixtral 8x7B.

通过创新自动生成系统，本文提出了两种新方法：一种基于强化学习的算法用于识别和排序与任务相关的列，另一种基于单元格相似性的方法用于增强少样例选择，该方法在 66 个数据集上经过了广泛测试，并使用两个不同的大型语言模型（Google flan-t5-xxl 和 Mixtral 8x7B）在数据填充、错误检测和实体匹配三个下游任务中展现出改进的性能。

表格数据任务的自动提示生成系统

An Automatic Prompt Generation System for Tabular Data Tasks

Deep neural networks (DNNs) are currently widely used for many artificial
intelligence (AI) applications including computer vision, speech recognition,
and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it
comes at the cost of high computational complexity. Accordingly, techniques
that enable efficient processing of DNNs to improve energy efficiency and
throughput without sacrificing application accuracy or increasing hardware cost
are critical to the wide deployment of DNNs in AI systems.
This article aims to provide a comprehensive tutorial and survey about the
recent advances towards the goal of enabling efficient processing of DNNs.
Specifically, it will provide an overview of DNNs, discuss various hardware
platforms and architectures that support DNNs, and highlight key trends in
reducing the computation cost of DNNs either solely via hardware design changes
or via joint hardware design and DNN algorithm changes. It will also summarize
various development resources that enable researchers and practitioners to
quickly get started in this field, and highlight important benchmarking metrics
and design considerations that should be used for evaluating the rapidly
growing number of DNN hardware designs, optionally including algorithmic
co-designs, being proposed in academia and industry.
The reader will take away the following concepts from this article:
understand the key design considerations for DNNs; be able to evaluate
different DNN hardware implementations with benchmarks and comparison metrics;
understand the trade-offs between various hardware architectures and platforms;
be able to evaluate the utility of various DNN design techniques for efficient
processing; and understand recent implementation trends and opportunities.

本文综述了目前深度神经网络的应用及其在计算机视觉、语音识别、机器人等 AI 任务中的高精度表现，同时也阐述了深度神经网络大规模部署所面临的高算力成本和能效问题，并提出了硬件设计、算法优化等多种解决方案以提高能效和算力，最后对比了各种深度神经网络的设计指标并展示了很多发展资源。