Language large models (LLMs) have made their way into fields ranging from Natural Language Processing (NLP) to Natural Language Understanding (NLU) to Natural Language Generation (NLG). LLMs like ChatGPT are gaining massive popularity, with over 1 million users since its release. With an overwhelming number of capabilities and applications, a new research paper, improved or developed model is released every day.
In a recent paper, the authors discuss large language models (LLMs) and a practical guide for practitioners and end users working with LLMs in their natural NLP tasks. It covered everything, including the uses of LLM, such as models, data, and downstream tasks. The main drive is to understand the working and use of the LLM and to have a working understanding of its applications, limitations and types of tasks in order to use it efficiently and effectively. The paper contains a guide on how and when to use the best suitable MSc programme.
The team discussed the three main types of data that are important to LLM: pre-training data, training/control data, and test data. The importance of high-quality data for training and testing LLMs and the impact of data biases on LLMs is also mentioned. The paper provided insight into best practices for working with LLM from a data perspective.
🚀 Join the fastest ML Subreddit community
The authors mainly focused on the applicability of LLMs to various NLP tasks, including knowledge-intensive tasks, traditional natural language understanding (NLU) tasks, and generative tasks. The authors provide detailed examples to highlight both successful use cases and limitations of the LLM in practice. They also discuss the emerging capabilities of the LLM, such as its ability to perform tasks beyond the original training data and the challenges associated with deploying the LLM in real-world scenarios.
The main contribution is summarized as follows –
- Natural Language Understanding – the LLM has an exceptional ability to generalize, which allows it to perform well on data outside of a distribution or with very few training examples
- Natural Language Generation – The LLM has the ability to generate coherent, contextually relevant, high quality text.
- Cognitive Intensive Tasks – The LLM has stored extensive knowledge that can be used for tasks that require domain-specific expertise or general knowledge of the world.
- Reasoning Ability – The authors stress the importance of understanding and harnessing the thinking capabilities of LLMs in order to realize their full potential in applications such as decision support systems and problem solving.
Overall, the paper is a great guide to see the practical applications of the LLM and its unique capabilities. It is important to know the limitations and use cases of an LLM before you start using it, so this research paper is definitely a great addition to the field of LLM.
scan the paper And github link. Don’t forget to join 20k+ML Sub RedditAnd discord channelAnd Email newsletter, where we share the latest AI research news, cool AI projects, and more. If you have any questions regarding the above article or if we’ve missed anything, feel free to email us at Asif@marktechpost.com
🚀 Check out 100’s AI Tools in the AI Tools Club
Tania Malhotra is a final year from University of Petroleum and Energy Studies, Dehradun, pursuing a BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is passionate about data science and has good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.