In this paper, we explore the question of whether language models (LLMs) can
support cost-efficient information extraction from complex tables. We introduce
schema-driven information extraction, a new task that uses LLMs to transform
tabular data into structured records following a human-authored schema. To
assess various LLM's capabilities on this task, we develop a benchmark composed
of tables from three diverse domains: machine learning papers, chemistry
tables, and webpages. Accompanying the benchmark, we present InstrucTE, a table
extraction method based on instruction-tuned LLMs. This method necessitates
only a human-constructed extraction schema, and incorporates an error-recovery
strategy. Notably, InstrucTE demonstrates competitive performance without
task-specific labels, achieving an F1 score ranging from 72.3 to 95.7.
Moreover, we validate the feasibility of distilling more compact table
extraction models to minimize extraction costs and reduce API reliance. This
study paves the way for the future development of instruction-following models
for cost-efficient table extraction.

本论文探讨了语言模型是否能够支持从复杂表格中进行成本有效的信息提取，并引入了基于架构驱动信息提取的新任务，使用 LLMs 将表格数据转换成遵循人类撰写的架构的结构化记录，并发展了一种 InstrucTE 方法来实现表格抽取，该方法仅需要人工构建的抽取架构，并具备错误恢复策略及高性能，证实了使用更紧凑的抽取模型进行抽取的可行性。