Parameters in Large Language Models (LLMs) are statistical weights that are adjusted during the training process. Here’s a comprehensive explanation:

Parameters: Parameters are the coefficients in the neural network that are learned from the training data. They determine how input data is transformed into output.

Significance: The number of parameters in an LLM is a key factor in its capacity to model complex patterns in data. More parameters generally mean a more powerful model, but also require more computational resources.

Role in LLMs: In LLMs, parameters are used to capture linguistic patterns and relationships, enabling the model to generate coherent and contextually appropriate language.

References:

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog.