Instruct and Extract: Instruction Tuning for On-Demand Information Extraction

Large language models with instructionfollowing 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 userspecified 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 .