Summary of On Uncertainty in Natural Language Processing, by Dennis Ulmer
On Uncertainty In Natural Language Processing
by Dennis Ulmer
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| 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 recent paper in natural language processing (NLP) aims to address the reliability and uncertainty of deep learning models used in various applications. The study focuses on large language models, which have revolutionized NLP over the past decade and are increasingly being deployed in user-facing systems. To achieve this, the authors aim to quantify model predictions’ reliability and uncertainties surrounding their development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has made tremendous progress in recent years, leading to powerful AI systems that are now used in many different areas. In language processing, some big breakthroughs have happened, including super-smart language models. These models are being used more and more in things people interact with directly. To get the benefits of this tech and avoid any potential problems, it’s important to figure out how reliable these predictions are and what’s not certain about them. |
Keywords
* Artificial intelligence * Deep learning * Natural language processing * Nlp
