Beyond the Buzz: Uncovering the Hidden Impact of LLMs on Our Future
The abilities of LLMs to solve diverse tasks with human-level performance come at the cost of slow training and inference, extensive hardware requirements and higher running costs. Such constraints are hard to accept and that led to better architectures and training strategies. Parameter efficient tuning, pruning, quantization, knowledge distillation and context length interpolation are some of the methods widely studied for efficient LLM utilization.
The strengths and, more importantly, the weaknesses of these language models are not yet fully explored. Industry benchmarks are crucially important as we navigate to the world that really exists.
