🔗 Right here is my straightforward to understand Knowledge Science/ML course YouTube playlist:
https://www.youtube.com/playlist?list=PLJXHwy-4vGRZauaA3D6pCS5drNfuMMSt5
🔗 Right here is my straightforward to understand ML workshop sequence:
https://www.youtube.com/playlist?list=PLJXHwy-4vGRbLixeEJ8dQsOAeVZdBAFUz
A Massive Language Mannequin (LLM) is a machine studying mannequin designed to know and generate human language. It’s a class of neural community fashions educated on giant quantities of textual content knowledge, reminiscent of books, articles, and different written paperwork, to study the statistical patterns and relationships between phrases and phrases in pure language.
Sections within the video:
0:00 – What’s a Massive Language Mannequin (LLM)
0:24 – What strategies do LLMs use?
0:54 – GPT-3
1:23 – ChatGPT: What’s the distinction between GPT-3 and ChatGPT?
2:00 – BERT
2:30 – XLNet
2:54 – T5
3:33 – RoBERTa
4:04 – Benefit and Limitations of utilizing a Massive Language Mannequin (LLM)
4:44 – Accountable AI
A number of Massive Language Fashions (LLMs) have been developed in recent times. On this video I present examples of some.
GPT-3 (Generative Pre-trained Transformer 3): It’s developed by OpenAI. GPT-3 is without doubt one of the largest and most superior LLMs presently out there. It has over 175 billion parameters. It’s able to producing extremely coherent textual content. GPT-3 has been used for a variety of purposes, reminiscent of chatbots, content material creation, and language translation.
ChatGPT: There was a buzz round ChatGPT. Many confuse ChatGPT and GPT-3 to be the identical. They’re truly not the identical. Whereas ChatGPT and GPT-3 share similarities of their underlying structure, they’re distinct fashions with totally different traits. ChatGPT is designed particularly for conversational AI, whereas GPT-3 is a extra general-purpose language mannequin that may be utilized to numerous pure language processing duties.
BERT (Bidirectional Encoder Representations from Transformers): It’s developed by Google. BERT is a robust LLM able to understanding the context of phrases and phrases in pure language. It has been used for numerous purposes, together with question-answering and sentiment evaluation. Considered one of my Ph.D. college students makes use of BERT-generated vectors in his analysis to create coherent tales from information articles.
XLNet (eXtreme MultiLingual Language Mannequin): It was developed by Carnegie Mellon College and Google. XLNet is an LLM that makes use of an autoregressive mannequin to generate textual content. It might probably generate high-quality textual content in a number of languages and has been used for purposes reminiscent of language translation and content material creation.
T5 (Textual content-to-Textual content Switch Transformer): A whole lot of T’s, probably not, 5 bushes. Developed by Google, T5 is an LLM able to producing a variety of pure language outputs, together with translation, summarization, and question-answering. It has been used for purposes reminiscent of language modeling and conversational brokers. A extra superior model of T5 is already launched and is named T5X. All huge tech corporations want an #LLM these days, after all, having one brings status. So, Fb, sorry Meta, wants one too. Sure, Meta’s LLM is RoBERTa.
RoBERTa (Robustly Optimized BERT pre-training Method): RoBERTa is an LLM that builds on the BERT structure to enhance efficiency on numerous pure language processing duties.
Dr. Shahriar Hossain
#languagemodels #languagemodel #llm #openai #gpt #bert
