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Large language models with instruction-following capabilities open the doorto a wider group of users. However, when it comes to information extraction - aclassic task in natural language processing - most task-specific systems cannotalign well with long-tail ad hoc extraction use cases for non-expert users. Toaddress this, we propose a novel paradigm, termed On-Demand InformationExtraction, to fulfill the personalized demands of real-world users. Our taskaims to follow the instructions to extract the desired content from theassociated text and present it in a structured tabular format. The tableheaders can either be user-specified or inferred contextually by the model. Tofacilitate research in this emerging area, we present a benchmark namedInstructIE, inclusive of both automatically generated training data, as well asthe human-annotated test set. Building on InstructIE, we further develop anOn-Demand Information Extractor, ODIE. Comprehensive evaluations on ourbenchmark reveal that ODIE substantially outperforms the existing open-sourcemodels of similar size. Our code and dataset are released onhttps://github.com/yzjiao/On-Demand-IE.

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