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The research explores the steerability of Large Language Models (LLMs),particularly OpenAI's ChatGPT iterations. By employing a behavioral psychologyframework called OCEAN (Openness, Conscientiousness, Extroversion,Agreeableness, Neuroticism), we quantitatively gauged the model'sresponsiveness to tailored prompts. When asked to generate text mimicking anextroverted personality, OCEAN scored the language alignment to that behavioraltrait. In our analysis, while "openness" presented linguistic ambiguity,"conscientiousness" and "neuroticism" were distinctly evoked in the OCEANframework, with "extroversion" and "agreeableness" showcasing a notable overlapyet distinct separation from other traits. Our findings underscore GPT'sversatility and ability to discern and adapt to nuanced instructions.Furthermore, historical figure simulations highlighted the LLM's capacity tointernalize and project instructible personas, precisely replicating theirphilosophies and dialogic styles. However, the rapid advancements in LLMcapabilities and the opaque nature of some training techniques make metricproposals degrade rapidly. Our research emphasizes a quantitative role todescribe steerability in LLMs, presenting both its promise and areas forfurther refinement in aligning its progress to human intentions.

AI Text-to-Behavior: A Study In Steerability