INSTRUCTIE: A Comprehensive Bilingual Dataset for Instruction-based Information Extraction

Core Concepts

The INSTRUCTIE dataset provides a comprehensive bilingual (Chinese and English) resource for training large language models to perform instruction-based information extraction tasks across diverse domains.

Abstract

The INSTRUCTIE dataset is introduced as a solution to address the limitations of existing information extraction (IE) datasets, which often have limited coverage and high construction costs. The dataset is constructed using the KG2Instruction framework, which automatically generates relational triples by aligning knowledge graphs with existing corpora, supplementing missing triples with a trained IE model, and filtering out unreal triples using natural language inference. The INSTRUCTIE dataset covers 12 diverse domains and 123 types of relations, containing 174,670 Chinese instances and 189,406 English instances. The authors evaluate various large language models (e.g., Baichuan2, LLaMA2, mT5) on INSTRUCTIE under multiple settings, including zero-shot learning, in-context learning, and fine-tuning. The results demonstrate that large language models fine-tuned on INSTRUCTIE can enhance their performance in instruction-based IE tasks and show certain advantages in generalizing to other domains. The authors also conduct an in-depth analysis of the dataset, including ablation studies on the KG2Instruction framework, evaluation of the models' generalization to unseen schemas, and error analysis. The findings suggest that the incorporation of LLMs and natural language inference models in the data generation process significantly improves the quality of the dataset, and the instruction-tuned models exhibit improved performance in entity recognition and relation extraction tasks.

Stats

The INSTRUCTIE dataset covers 12 diverse domains and 123 types of relations. The dataset contains 174,670 Chinese instances and 189,406 English instances. The average number of triples per instance ranges from 1.78 to 11.96, depending on the domain. The average token count per instance ranges from 80.67 to 267.37, depending on the domain.

Quotes

"Large language models can perform well on general natural language tasks, but their effectiveness is still not optimal for information extraction." "To address this issue, we introduce INSTRUCTIE, a bilingual instruction-based information extraction dataset, which covers 12 diverse domains." "Experimental results demonstrate that large language models trained with INSTRUCTIE can not only obtain better information extraction capabilities but also enhance zero-shot performance compared with baselines."

Key Insights Distilled From

InstructIE: A Bilingual Instruction-based Information Extraction Dataset

by Honghao Gui,... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2305.11527.pdf

Deeper Inquiries

How can the KG2Instruction framework be further improved to generate higher-quality datasets with even broader domain coverage?

The KG2Instruction framework can be enhanced in several ways to generate datasets of higher quality with broader domain coverage: Enhanced Entity Recognition: Improving the entity recognition component of the framework can lead to more accurate identification of entities in text, resulting in better quality datasets. This can involve fine-tuning existing entity recognition models or incorporating more advanced techniques like entity linking to disambiguate entities. Schema Expansion: To broaden domain coverage, the framework can be modified to automatically identify and incorporate new schemas from diverse domains. This can involve leveraging external knowledge bases or ontologies to enrich the schema repository used in dataset generation. Multi-lingual Support: Expanding the framework to support more languages can significantly increase the diversity and coverage of the generated datasets. This can be achieved by incorporating multilingual models for entity recognition and relation extraction. Quality Control Mechanisms: Implementing robust quality control mechanisms, such as additional rounds of human annotation or automated validation checks, can help ensure the datasets generated are of high quality and free from errors. Domain-specific Tuning: Introducing domain-specific tuning mechanisms within the framework can enable the generation of datasets tailored to specific industries or fields, further enhancing the relevance and applicability of the datasets. Collaborative Framework: Facilitating collaboration with domain experts and researchers to provide feedback and domain-specific insights can help refine the dataset generation process and ensure the datasets meet the requirements of various applications.

How can the potential challenges and limitations of using large language models for instruction-based information extraction tasks be addressed?

Large language models (LLMs) pose several challenges and limitations in instruction-based information extraction tasks, including: Data Efficiency: LLMs require large amounts of annotated data for training, which can be costly and time-consuming to acquire. Addressing this challenge involves exploring techniques like data augmentation, transfer learning, and active learning to make the most of limited annotated data. Interpretability: LLMs often lack interpretability, making it challenging to understand how they arrive at specific extraction decisions. Techniques like attention visualization, explanation generation, and model distillation can help improve interpretability. Bias and Fairness: LLMs are susceptible to biases present in the training data, leading to biased extraction results. Mitigating bias involves careful data preprocessing, bias detection algorithms, and fairness-aware training strategies. Domain Adaptation: LLMs may struggle with generalizing to new or unseen domains. Domain adaptation techniques, domain-specific fine-tuning, and transfer learning can help improve performance across diverse domains. Scalability: Scaling LLMs for large-scale datasets and real-time applications can be challenging. Optimizing model architecture, leveraging distributed computing, and efficient model serving infrastructure can address scalability issues. Ethical Considerations: Ensuring ethical use of LLMs in information extraction tasks involves addressing privacy concerns, data security, and transparency in model deployment. Adhering to ethical guidelines and regulations is crucial in mitigating potential risks.

How can the INSTRUCTIE dataset be leveraged to advance research in knowledge graph construction, question-answering systems, and other applications that rely on structured data extraction from text?

The INSTRUCTIE dataset can be instrumental in advancing research in various areas: Knowledge Graph Construction: Researchers can use INSTRUCTIE to train models for automatic knowledge graph construction, improving the accuracy and coverage of extracted information. The dataset can facilitate the creation of more comprehensive and structured knowledge graphs across diverse domains. Question-Answering Systems: INSTRUCTIE can serve as a valuable resource for training question-answering systems, enabling them to extract relevant information from text and provide accurate answers to user queries. The dataset can enhance the performance of question-answering models by providing high-quality training data. Information Retrieval: Leveraging the structured data in INSTRUCTIE, researchers can develop advanced information retrieval systems that retrieve specific information from unstructured text sources. This can improve search accuracy and efficiency in retrieving relevant information. Natural Language Understanding: By using the dataset for training natural language understanding models, researchers can enhance the models' ability to interpret and extract information from complex instructions, leading to improved performance in various NLP tasks. Domain-specific Applications: Researchers can tailor models trained on INSTRUCTIE to specific domains such as healthcare, finance, or legal, enabling the development of domain-specific applications that require structured data extraction from text. Overall, the INSTRUCTIE dataset provides a rich resource for advancing research in knowledge extraction, question-answering, and other NLP applications, paving the way for innovative solutions in information retrieval and knowledge representation.