LLMs

Though recent advances have been made in large language model (LLM) reasoning, LLMs still have a hard time with hierarchical multi-step reasoning tasks like developing sophisticated programs. Human programmers, in contrast to other token generators, have (usually) learned to break down difficult tasks into manageable components that work alone (modular) and work together (compositional). As a bonus, if human-generated tokens cause problems with a function, it should be possible to rewrite that part of the software without affecting the rest of the application. In contrast, it is naively anticipated that code LLMs will produce token sequences free from errors. This

Google's new technology, Confident Adaptive Language Modeling (CALM), speeds up large language models (LLM) upto 3 times.

These are my ideas and predictions about large language models (LLMs) and the way they will change the tech world forever.

Nvidia revs its NeMo Megatron LLM stack, implementing novel techniques and a new tool to speed training and enable larger models