Generative AI exists because of the transformer

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• Humans are also used to provide feedback and fill gaps in information — a process known as reinforcement learning by human feedback (RLHF)

• Transformers process an entire sequence at once

• A key concept of the transformer architecture is self-attention. This is what allows LLMs to understand relationships between words.

• Self-attention looks at each token in a body of text and decides which others are most important to understanding its meaning

• Transformers use a number of approaches to address this problem and enhance the quality of their output. One example is called beam search.

• While the text may seem plausible and coherent, it isn’t always factually correct. LLMs are not search engines looking up facts; they are pattern-spotting engines that guess the next best option in a sequence.

• Although researchers say hallucinations will never be completely erased, Google, OpenAI and others are working on limiting them through a process known as “grounding”

• The LLM is underpinned by a scientific development known as the transformer model, made by Google researchers in 2017

• This allows the software to capture context and patterns better

• Before transformers, the state of the art AI translation methods were recurrent neural networks (RNNs), which scanned each word in a sentence and processed it sequentially.

• But this method of predicting the following word in isolation — known as “greedy search” — can introduce problems. Sometimes, while each individual token might be the next best fit, the full phrase can be less relevant.

• Rather than focusing only on the next word in a sequence, it looks at the probability of a larger set of tokens as a whole.

• Because of this inherent predictive nature, LLMs can also fabricate information in a process that researchers call “hallucination”. They can generate made-up numbers, names, dates, quotes — even web links or entire articles.

• Its real power, however, lies beyond language. Its inventors discovered that transformer models could recognise and predict any repeating motifs or patterns.

• It could even predict notes in music and DNA in proteins to help design drug molecules.

• Generative AI exists because of the transformer

• But this alone is not what makes LLMs so clever. What unlocked their abilities to parse and write as fluently as they do today is a tool called the transformer, which radically sped up and augmented how computers understood language.

• Research outlining the transformer model was first published by a group of eight AI researchers at Google in June 2017. Their 11-page research paper marked the start of the generative AI era.

• GPT-4 can generate and ingest large volumes of text: users can feed in up to 25,000 English words, which means it could handle detailed financial documentation, literary works or technical manuals.

• From this enormous corpus of words and images, the models learn how to recognise patterns and eventually predict the next best word

Toughts & Comments

Indranil

Check the original papers of Google.