At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their … See more

Because machine learning algorithms process numbers rather than text, the text must be converted to numbers. In the first step, a vocabulary is decided upon, then integer indexes are arbitrarily but uniquely assigned t… See more

Reinforcement learning from human feedback (RLHF) through algorithms, such as proximal policy optimization, is used to further fine-tune a model based on a dataset of human preferences.
Using "self … See more

Advances in software and hardware have reduced the cost substantially since 2020, such that in 2023 training of a 12-billion-parameter LLM computational cost is 72,300 A100-GPU-hours, while in 2020 the cost of train… See more

There are certain tasks that, in principle, cannot be solved by any LLM, at least not without the use of external tools or additional software. An example of such a task is responding to the user's input '354 * 139 = ', pro… See more

An LLM is a language model, which is not an agent as it has no goal, but it can be used as a component of an intelligent agent. Researchers have described several methods for such integrations.
The ReAct … See more

Typically, LLM are trained with full- or half-precision floating point numbers (float32 and float16). One float16 has 16 bits, or 2 bytes, and so one billion parameters require 2 gigabytes. The largest models typically ha… See more