to worker failures and preemptions. At inference time, only a single path needs to be executed for each input, without the need for any model compression. We...
In generation models, higher quality is generally found through feedback methods. Because token-generation is greedy, or it generally maximizes the...
TODO: THorough research and add /change with optimization/index.md
The big-bang like expansion of AI has led to a surge in services, methods, frameworks, and tools that enhance the creation and deployment of models from...
Grounding is the opposite of hallucination and confabulation: it means a model's output is actually tied to real, verifiable facts rather than...
How large language models learn from vast corpora of unlabelled data
The new scaling paradigm — trading inference compute for accuracy
recursive training involves the use of an LLM so improve the selection or variety of data for that LLM. This is well describe in [data augmentation](../../data/augmentation/index.md)
In generative AI, the raw data—whether it be in text or binary input is divided into individual units termed as *tokens*. These are then made into IDs that provide a lookup table that can be used in downstream learning that allow for context aware [embedding...