Large Language Models – An overview
Large Language Models are offering numerous advantages for organisations who want to use AI to bring efficiencies to their workflows. But this has not always been the case.
For the last few years, language-based Artificial Intelligence models have come to the forefront of Natural Language Processing (NLP). In simple terms, a language model can be derived as a probability distribution over a sequence of words. It assigns a probability to a piece of unseen text, based on some training data.
The capabilities of a language model vary from simple analytical tasks such as sentiment analysis, spell checking, translations between languages etc. to more advanced features; question and answering, speech recognition, text summarization, semantic search and many more. Voice assistants like Siri, Alexa, Google Translator, and search engines like Google Search, and Bing are the biggest and most familiar examples that showcase the power of language models.
As a result of continuous research, academic institutions, and big tech companies such as OpenAI, Microsoft and Nvidia have come up with more improved versions of these simple language models. They are building more intelligent systems with a richer understanding of language extending the capabilities of existing models. So, these latest high performing language models are now called large language models (LLM).
As the name reflects these models are larger in sheer size. Firstly, the enormous amounts of data (covering different styles, such as user-generated content, news data and literature which amounts to billions) on which they have trained. Also, the wide range of tasks which can be applied to create smarter platforms with a greater processing speed. The most fascinating thing about the LLM is that with large models there is no need to start from scratch, train and finetune with costly clusters of servers.
Large models are capable of recognizing things that haven’t explicitly been seen during training (zero-shot scenarios) or use with fine-tuning, based on a specific domain with a minimal amount of data (few-shot scenarios). Recently, companies like Nvidia, Microsoft, and Open AI have taken steps to release API access to these LLMs making them accessible and affordable for everyone. Not only that, the usages of LLM have grown dramatically and the models are able to question and answer with tabular data, content generation, image generation, code completion and more.
Among LLMs, BERT and GPT can be pointed out as the most capable and easily accessible model families in the market.
Large Language Models: BERT
Bidirectional Encoder Representations from Transformers (BERT) is a transformer language model developed by Google. BERT was one of the first solutions to dominate the market with transformer architecture in 2018.
Amongst many applications, BERT is able to:
- Determine how positive or negative a set of comments or reviews are (Sentiment Analysis)
- Help chatbots answer your questions (Question and Answering)
- Predict your text when writing an email or word processor (Text prediction)
- Write an article about any topic with just a few sentence inputs (Text generation)
- Quickly summarize long legal contracts (Summarization)
Interestingly, unlike many other LLMs, BERT is open source, allowing developers to run models quickly without spending fortunes on development. The variants of BERT (SpanBERT, DistilBERT, TinyBERT, ALBERT, RoBERTa and ELECTRA) are special versions which are intelligently optimized to outperform the drawbacks of BERT.
Large Language Models: GPT
The Generative Pre-Trained Transformer (GPT) model was first introduced in 2018 by OpenAI. The powerful performance of the model with few or zero unlabeled data based on Autoregressive Transformer language intrigued the NLP community at that time.
After the initial release, further iterations appeared. GPT-3 appeared in 2020 and was the highest performance LLM at the time, being 1,000 times the size of GPT-1.
GPT3 can perform:
- Text classification
- Question answering
- Text generation
- Text summarization
- Named-entity recognition
- Language translation
This is a version which can be used even without fine-tuning the model.
GPT-Neo, GPT-J, and GPT-NeoX are the other variants of this family that were trained and released by EleutherAI. Open-source versions of GPT-3 which are released by Open-AI now available for affordable prices.
Google researchers in February 2022 published a model far smaller than GPT-3, called Fine-tuned Language Net (FLAN) which beats GPT3 on a number of challenging benchmarks. It outperformed GPT-3 on 19 out of the 25 tasks as well as performance on 10 tasks.
Large Language Models: In Conclusion
The community of Large Language Models is unstoppable. Their capability is proven and has proven benefits. Use of LLMs is widespread and mainstream. They are extremely accessible since they exist increasingly as open-source solutions. LLMs continue to evolve with new research and technology.
Large Language Models and Deep Data Insight
Deep Data Insight have been at the forefront of using LLMs to transform their customers’ experiences. Key benefits are to the efficiencies of workflows, where high-cost manual time is replaced with an LLM model.
DDI’s emerging ‘Document AI’ platform will ensure that all data extracting functions will be available through a single platform. With Document AI, LLMs are used to extract data from a given document using the LLMs Question and Answering feature. This means that the whole document does not have to be reviewed – instead just ask a question to find and extract the relevant documented information. This can even be used to find and extract data from tables in CSV or Excel files.
The platform will also allow a user to search across a variety of documents to find the one that is most relevant for any given keyword.
DDI are active in a number of sectors, including healthcare, real estate and insurance. These are all sectors that Platform AI will benefit with its pioneering application of LLMs.
For information about how we are successfully helping our clients achieve amazing ROI in their workflows, take a look at our case studies here: https://www.deepdatainsight.com/case-studies/
