A Mathematical Abstraction for Balancing the Trade-off Between
  Creativity and Reality in Large Language Models
Ritwik Sinha Zhao Song Tianyi Zhou
Large Language Models have become popular for their remarkable capabilities
in human-oriented tasks and traditional natural language processing tasks. Its
efficient functioning is attributed to the attention mechanism in the
Transformer architecture, enabling it to concentrate on particular aspects of
the input.
  LLMs are increasingly being used in domains such as generating prose, poetry
or art, which require the model to be creative (e.g. Adobe firefly). LLMs
possess advanced language generation abilities that enable them to generate
distinctive and captivating content. This utilization of LLMs in generating
narratives shows their flexibility and potential for use in domains that extend
beyond conventional natural language processing duties.
  In different contexts, we may expect the LLM to generate factually correct
answers, that match reality; e.g., question-answering systems or online
assistants. In such situations, being correct is critical to LLMs being trusted
in practice. The Bing Chatbot provides its users with the flexibility to select
one of the three output modes: creative, balanced, and precise. Each mode
emphasizes creativity and factual accuracy differently.
  In this work, we provide a mathematical abstraction to describe creativity
and reality based on certain losses. A model trained on these losses balances
the trade-off between the creativity and reality of the model.