  The research explores the steerability of Large Language Models (LLMs),
particularly OpenAI's ChatGPT iterations. By employing a behavioral psychology
framework called OCEAN (Openness, Conscientiousness, Extroversion,
Agreeableness, Neuroticism), we quantitatively gauged the model's
responsiveness to tailored prompts. When asked to generate text mimicking an
extroverted personality, OCEAN scored the language alignment to that behavioral
trait. In our analysis, while "openness" presented linguistic ambiguity,
"conscientiousness" and "neuroticism" were distinctly evoked in the OCEAN
framework, with "extroversion" and "agreeableness" showcasing a notable overlap
yet distinct separation from other traits. Our findings underscore GPT's
versatility and ability to discern and adapt to nuanced instructions.
Furthermore, historical figure simulations highlighted the LLM's capacity to
internalize and project instructible personas, precisely replicating their
philosophies and dialogic styles. However, the rapid advancements in LLM
capabilities and the opaque nature of some training techniques make metric
proposals degrade rapidly. Our research emphasizes a quantitative role to
describe steerability in LLMs, presenting both its promise and areas for
further refinement in aligning its progress to human intentions.
