Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Submission Type: Regular Long Paper
Submission Track: Information Extraction
Submission Track 2: Information Retrieval and Text Mining
Keywords: information extraction, instruction-tuning, language model, open scenario
TL;DR: We propose a novel paradigm called On-Demand Information Extraction and the corresponding dataset, InstructIE, that enables personalized extraction of desired content from text.
Abstract: 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.
Submission Number: 5175
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