Column type annotation is the task of annotating the columns of a relational
table with the semantic type of the values contained in each column. Column
type annotation is a crucial pre-processing step for data search and
integration in the context of data lakes. State-of-the-art column type
annotation methods either rely on matching table columns to properties of a
knowledge graph or fine-tune pre-trained language models such as BERT for the
column type annotation task. In this work, we take a different approach and
explore using ChatGPT for column type annotation. We evaluate different prompt
designs in zero- and few-shot settings and experiment with providing task
definitions and detailed instructions to the model. We further implement a
two-step table annotation pipeline which first determines the class of the
entities described in the table and depending on this class asks ChatGPT to
annotate columns using only the relevant subset of the overall vocabulary.
Using instructions as well as the two-step pipeline, ChatGPT reaches F1 scores
of over 85% in zero- and one-shot setups. To reach a similar F1 score a RoBERTa
model needs to be fine-tuned with 300 examples. This comparison shows that
ChatGPT is able deliver competitive results for the column type annotation task
given no or only a minimal amount of task-specific demonstrations.

探索使用 ChatGPT 实现列类型注释，通过零或少量示范，ChatGPT 利用指令和两步注释管道的方法可以实现零样本和一次样本的 F1 分数超过 85％，达到与 RoBERTa 模型类似的精度，而后者需要 300 个示例。

使用 ChatGPT 进行列类型注释

Column Type Annotation using ChatGPT

The success of deep learning has sparked interest in improving relational
table tasks, like data preparation and search, with table representation models
trained on large table corpora. Existing table corpora primarily contain tables
extracted from HTML pages, limiting the capability to represent offline
database tables. To train and evaluate high-capacity models for applications
beyond the Web, we need resources with tables that resemble relational database
tables. Here we introduce GitTables, a corpus of 1M relational tables extracted
from GitHub. Our continuing curation aims at growing the corpus to at least 10M
tables. Analyses of GitTables show that its structure, content, and topical
coverage differ significantly from existing table corpora. We annotate table
columns in GitTables with semantic types, hierarchical relations and
descriptions from Schema.org and DBpedia. The evaluation of our annotation
pipeline on the T2Dv2 benchmark illustrates that our approach provides results
on par with human annotations. We present three applications of GitTables,
demonstrating its value for learned semantic type detection models, schema
completion methods, and benchmarks for table-to-KG matching, data search, and
preparation. We make the corpus and code available at
this https URL

通过从 GitHub 中提取的 GitTables 数据集，我们可以训练和评估应用于深度学习的高容量模型，以实现表征模型与关系数据库表相似的表。通过进行语义类型标注、层次关系和来自 Schema.org 和 DBpedia 的描述，我们可以提供人工注释一致的结果，并通过三个应用：语义类型检测模型、模式完成方法以及用于表到知识图谱匹配的基准测试，展示 GitTables 的价值。

GitTables：关系表的大规模语料库

GitTables: A Large-Scale Corpus of Relational Tables

Transfer learning has proven to be a successful technique to train deep
learning models in the domains where little training data is available. The
dominant approach is to pretrain a model on a large generic dataset such as
ImageNet and finetune its weights on the target domain. However, in the new era
of an ever-increasing number of massive datasets, selecting the relevant data
for pretraining is a critical issue. We introduce Neural Data Server (NDS), a
large-scale search engine for finding the most useful transfer learning data to
the target domain. NDS consists of a dataserver which indexes several large
popular image datasets, and aims to recommend data to a client, an end-user
with a target application with its own small labeled dataset. The dataserver
represents large datasets with a much more compact mixture-of-experts model,
and employs it to perform data search in a series of dataserver-client
transactions at a low computational cost. We show the effectiveness of NDS in
various transfer learning scenarios, demonstrating state-of-the-art performance
on several target datasets and tasks such as image classification, object
detection and instance segmentation. Neural Data Server is available as a
web-service at this http URL

介绍了一种基于神经网络的数据搜索方法 Neural Data Server (NDS)，该服务可以在大规模的图像数据中为用户搜索最有用的迁移学习数据，用于在目标应用程序中进行图像分类、目标检测和实例分割等任务中得到最先进的性能。