GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks
This addresses the problem of computational expense and structured output limitations in large language models for information extraction, offering a more efficient alternative.
The paper tackles the need for accurate, efficient, and generalizable models in information extraction by introducing a lightweight encoder model that achieves state-of-the-art performance on zero-shot NER benchmarks and leading results on tasks like question-answering, summarization, and relation extraction.
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.