From Temperature to Top-p: Tuning Large Language Model Parameters for Better Results

Large Language Models (LLMs) are advanced machine learning models designed to understand and generate human language. They are based on the transformer architecture, which uses self-attention mechanisms to process and relate words in a sequence, allowing the model to capture long-range dependencies and contextual meanings more effectively than previous models like RNNs. This architecture enables LLMs to perform a wide variety of language tasks, such as translation, summarization, and text generation, by leveraging large-scale training data and vast amounts of computational power.

The parameters for any similar large language models, usually include tuning parameters related to the generation and fine-tuning processes. These parameters control various aspects of the model’s behavior and the quality of the outputs. Common parameters that can be adjusted include:

1. Temperature

• Definition: Controls the randomness of predictions by scaling the logits before applying softmax. Higher values (e.g., 1.5) make the model output more random, while lower values (e.g., 0.2) make it more deterministic.
• Effect: Higher temperature increases creativity but reduces coherence. Lower temperature results in more conservative and predictable outputs.
• High Temperature (1.5):
  “Once upon a time, a cat named Whiskers and a robot named Z3 found themselves on an intergalactic spaceship. Whiskers…