Instruct and Extract: Instruction Tuning for On-Demand Information
  Extraction
Yizhu Jiao Ming Zhong Sha Li Ruining Zhao Siru Ouyang Heng Ji Jiawei Han
Large language models with instruction-following capabilities open the door
to a wider group of users. However, when it comes to information extraction - a
classic task in natural language processing - most task-specific systems cannot
align well with long-tail ad hoc extraction use cases for non-expert users. To
address this, we propose a novel paradigm, termed On-Demand Information
Extraction, to fulfill the personalized demands of real-world users. Our task
aims to follow the instructions to extract the desired content from the
associated text and present it in a structured tabular format. The table
headers can either be user-specified or inferred contextually by the model. To
facilitate research in this emerging area, we present a benchmark named
InstructIE, inclusive of both automatically generated training data, as well as
the human-annotated test set. Building on InstructIE, we further develop an
On-Demand Information Extractor, ODIE. Comprehensive evaluations on our
benchmark reveal that ODIE substantially outperforms the existing open-source
models of similar size. Our code and dataset are released on
https://github.com/yzjiao/On-Demand-IE.