title: GLiNER multi-task: Generalist Lightweight Model for Various Information
    Extraction Tasks
summary: Information extraction tasks require both accurate, efficient, and
  generalisable models. Classical supervised deep learning approaches can achieve
  the required performance, but they need large datasets and are limited in their
  ability to adapt to different tasks. On the other hand, large language models
  (LLMs) demonstrate good generalization, meaning that they can adapt to many
  different tasks based on user requests. However, LLMs are computationally
  expensive and tend to fail to generate structured outputs. In this article, we
  will introduce a new kind of GLiNER model that can be used for various
  information extraction tasks while being a small encoder model. Our model
  achieved SoTA performance on zero-shot NER benchmarks and leading performance
  on question-answering, summarization and relation extraction tasks.
  Additionally, in this article, we will cover experimental results on
  self-learning approaches for named entity recognition using GLiNER models.
paper_authors._id: 667529f0246665be1aa7312a
paper_authors.name: Ihor Stepanov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T10:24:42.457Z
paper_authors._id: 62d59dd5a2de3ae5ea6fc262
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1658166666371-noauth.png
paper_authors.fullname: Stepanov
paper_authors.type: user
paper_authors.user: Ihor
paper_authors._id: 667529f0246665be1aa7312b
paper_authors.name: Mykhailo Shtopko
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T07:57:48.931Z
paper_authors._id: 6405f62ba577649430be5124
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6405f62ba577649430be5124/WMSqNvDqaeydFkHhNCU_K.png
paper_authors.fullname: Mykhailo Shtopko
paper_authors.type: user
paper_authors.user: BioMike
paper_title: GLiNER multi-task: Generalist Lightweight Model for Various Information
    Extraction Tasks
paper_summary: Information extraction tasks require both accurate, efficient, and
  generalisable models. Classical supervised deep learning approaches can achieve
  the required performance, but they need large datasets and are limited in their
  ability to adapt to different tasks. On the other hand, large language models
  (LLMs) demonstrate good generalization, meaning that they can adapt to many
  different tasks based on user requests. However, LLMs are computationally
  expensive and tend to fail to generate structured outputs. In this article, we
  will introduce a new kind of GLiNER model that can be used for various
  information extraction tasks while being a small encoder model. Our model
  achieved SoTA performance on zero-shot NER benchmarks and leading performance
  on question-answering, summarization and relation extraction tasks.
  Additionally, in this article, we will cover experimental results on
  self-learning approaches for named entity recognition using GLiNER models.
paper_ai_summary: A small encoder model, GLiNER, achieves state-of-the-art performance on zero-shot and various information extraction tasks, combining size efficiency with strong generalization.
paper_ai_keywords: GLiNER model zero-shot NER named entity recognition question-answering summarization relation extraction self-learning