Summary of Gliner Multi-task: Generalist Lightweight Model For Various Information Extraction Tasks, by Ihor Stepanov and Mykhailo Shtopko
GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks
by Ihor Stepanov, Mykhailo Shtopko
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to information extraction is introduced, combining the strengths of classical supervised deep learning and large language models (LLMs). While LLMs demonstrate good generalization and adaptability, they are computationally expensive and struggle with generating structured outputs. To address this, a new type of GLiNER model is proposed, which achieves state-of-the-art performance on zero-shot named entity recognition benchmarks and leading results on question-answering, summarization, and relation extraction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of computer model can do lots of different tasks like finding important words in text or answering questions. This model is special because it’s not too big and doesn’t need a lot of training data. It’s also good at doing things on its own without being told exactly what to do. The creators of this model tested it and found that it was really good at several tasks, including recognizing important words in text. |
Keywords
» Artificial intelligence » Deep learning » Generalization » Named entity recognition » Question answering » Summarization » Supervised » Zero shot
