Large language models process vast volumes of text data to approximate human speech. The LLM uses a deep learning model, a network of interconnected neurons, to process, analyze, and forecast complex data to produce these natural language responses.

"We have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination." - Andrew Ng,  AI leader and founder of DeepLearning.AI.

Many tasks in natural language processing (NLP), such as speech-to-text and sentiment analysis, rely on language models as their basis. These models can analyze a text and guess what word will come next. ChatGPT, LaMDA, PaLM, etc., are all examples of LLMs. The parameters of an LLM let it make predictions about the likelihood of word sequences by taking into account the text's relationships. With more parameters, the model can capture complex relationships and handle unusual words.

OpenAI

ChatGPT

ChatGPT is a free and open-source chatbot built on the GPT-3 linguistic framework. It can hold talks with humans using their language. ChatGPT has been taught to respond to questions, provide information, and generate original material across a wide range of topics. 

GPT-3 vs ChatGPT

• The more versatile GPT-3 model can be applied to other linguistic endeavours. Conversational tasks are where ChatGPT shines.
• ChatGPT uses fewer data in its training than GPT-3. 
• GPT-3 is superior to ChatGPT since it has 175B parameters instead of 1.5B parameters.

GPT-4

The Generative Pre-trained Transformer 4 (GPT-4) is OpenAI's fourth large multimodal language model in its GPT series. As a transformer, GPT-4 was pre-trained to predict the next token (using both public data and "data licensed from third-party providers"), and then fine-tuned with reinforcement learning based on human and AI feedback to ensure human alignment and policy conformance.

Google

LaMDA

LaMDA is a group of conversational models built on top of the Transformer framework. These models trained using 1.56T words of publicly available conversation data can include as many as 137B parameters. LaMBDA can have unstructured conversations about a wide variety of topics. It can follow the thread of a discussion rather than sticking to a rigid set of instructions, setting it apart from more conventional chatbots.

BARD

Bard is a chatbot that can mimic human interactions and answer queries using NLP and machine learning. LaMDA technology underpins it, and unlike ChatGPT, which uses data acquired exclusively through 2021, it can deliver timely insights.

PaLM

PaLM is a 540B parameter language model that can do sophisticated tasks, including learning and reasoning. In tests of language and logic, it can surpass both humans and the most advanced language models. The PaLM system generalizes from limited data using a few-shot learning technique to replicate how humans learn and solve new problems.

Deepmind

Gopher

Gopher outperforms state-of-the-art large language models in areas where such expertise is required, such as answering queries about niche fields like science and the humanities, and is on par with them in places where such expertise is not needed, such as in logical reasoning and mathematics. In addition, Gopher contains 280B tunable parameters, making it more robust than OpenAI's GPT-3 (175B).

Sparrow

DeepMind's Sparrow chatbot is programmed to provide suitable responses to user queries while minimizing potential threats. Sparrow was created to help fix language models that return misleading, biased, or destructive results. Sparrow is a language model that has been educated to be more helpful, correct, and harmless than the baseline of pre-trained language models by applying human judgements.

Meta

OPT-IML

OPT-IML is a pre-trained language model with 175 billion parameters based on Meta's OPT model. Using approximately 2000 natural language tasks, OPT-IML is optimized for improved performance on natural language tasks such as query answering, text summarization, and translation. As a result, it has a smaller CO2 footprint and is more efficient in training than OpenAI's GPT-3.

NVIDIA

Megatron-Turing NLG

The Megatron-Turing Natural Language Generation (MT-NLG) model is the most significant. It is a transformer-based language model with 530 billion parameters. It outperforms prior state-of-the-art models in zero-, one-, and few-shot settings and exhibits unmatched precision in natural language tasks such as completion prediction, intuitive reasoning, reading comprehension, natural language inferences, and word sense disambiguation.

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