Efficiently extracting data from tables in the scientific literature is
pivotal for building large-scale databases. However, the tables reported in
materials science papers exist in highly diverse forms; thus, rule-based
extractions are an ineffective approach. To overcome this challenge, we present
MaTableGPT, which is a GPT-based table data extractor from the materials
science literature. MaTableGPT features key strategies of table data
representation and table splitting for better GPT comprehension and filtering
hallucinated information through follow-up questions. When applied to a vast
volume of water splitting catalysis literature, MaTableGPT achieved an
extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive
evaluations of the GPT usage cost, labeling cost, and extraction accuracy for
the learning methods of zero-shot, few-shot and fine-tuning, we present a
Pareto-front mapping where the few-shot learning method was found to be the
most balanced solution owing to both its high extraction accuracy (total F1
score>95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of
10 I/O paired examples). The statistical analyses conducted on the database
generated by MaTableGPT revealed valuable insights into the distribution of the
overpotential and elemental utilization across the reported catalysts in the
water splitting literature.

利用 MaTableGPT 从材料科学文献中高效提取表格数据，成功实现了 96.8% 的提取准确率，并展示了几乎无错误的学习方法的成本分析和 Pareto 前沿映射，为水分解催化领域的研究提供了宝贵的洞察。

MaTableGPT: 材料科学文献中基于 GPT 的表格数据提取器

MaTableGPT: GPT-based Table Data Extractor from Materials Science  Literature

Documents that consist of diverse templates and exhibit complex spatial
structures pose a challenge for document entity classification. We propose
KNN-former, which incorporates a new kind of spatial bias in attention
calculation based on the K-nearest-neighbor (KNN) graph of document entities.
We limit entities' attention only to their local radius defined by the KNN
graph. We also use combinatorial matching to address the one-to-one mapping
property that exists in many documents, where one field has only one
corresponding entity. Moreover, our method is highly parameter-efficient
compared to existing approaches in terms of the number of trainable parameters.
Despite this, experiments across various datasets show our method outperforms
baselines in most entity types. Many real-world documents exhibit combinatorial
properties which can be leveraged as inductive biases to improve extraction
accuracy, but existing datasets do not cover these documents. To facilitate
future research into these types of documents, we release a new ID document
dataset that covers diverse templates and languages. We also release enhanced
annotations for an existing dataset.

我们提出了一种新的基于 K 最近邻图的空间偏差的注意力计算方法 KNN-former，该方法限制实体的注意力仅在 K 最近邻图定义的局部半径内；我们还使用组合匹配来解决多对一映射的问题，此外，与现有方法相比，我们的方法在可训练参数的数量方面更加高效。虽然如此，在各种数据集上的实验证明，我们的方法在大多数实体类型上优于基线方法。许多现实世界的文档具有组合特性，可以作为归纳偏差来提高提取准确性，但现有数据集没有涵盖这些文档。为了促进未来对这些类型的文档的研究，我们发布了一个涵盖多样化模板和语言的新 ID 文档数据集，并对现有数据集进行了增强的注释。

轻量级空间建模以从文件中提取组合信息

Lightweight Spatial Modeling for Combinatorial Information Extraction  From Documents

Most successful information extraction systems operate with access to a large
collection of documents. In this work, we explore the task of acquiring and
incorporating external evidence to improve extraction accuracy in domains where
the amount of training data is scarce. This process entails issuing search
queries, extraction from new sources and reconciliation of extracted values,
which are repeated until sufficient evidence is collected. We approach the
problem using a reinforcement learning framework where our model learns to
select optimal actions based on contextual information. We employ a deep
Q-network, trained to optimize a reward function that reflects extraction
accuracy while penalizing extra effort. Our experiments on two databases -- of
shooting incidents, and food adulteration cases -- demonstrate that our system
significantly outperforms traditional extractors and a competitive
meta-classifier baseline.

本文研究了在训练数据稀缺的情况下，通过获取和整合外部证据来提高信息提取系统的准确性的任务，并利用增强学习框架实现优化操作以提高精度的深度 Q 网络，在枪击事件和食品欺诈案件等两个数据库中实现了显著的性能提升。