ControlLM: Crafting Diverse Personalities for Language Models
Yixuan Weng Shizhu He Kang Liu Shengping Liu Jun Zhao
As language models continue to scale in size and capability, they display an
array of emerging behaviors, both beneficial and concerning. This heightens the
need to control model behaviors. We hope to be able to control the personality
traits of language models at the inference-time so as to have various character
features, on top of which the requirements of different types of tasks can be
met. Personality is a higher-level and more abstract behavioral representation
for language models. We introduce ControlLM, which leverages differential
activation patterns, derived from contrasting behavioral prompts in the model's
latent space, to influence the model's personality traits at inference. This
approach allows for the precise, real-time adjustment of model behavior. First,
we demonstrate ControlLM's capacity to elicit diverse persona behaviors without
any training, while precision control allows personality traits to closely
match average human values. Subsequently, we showcase improved reasoning and
question answering through selective amplification of beneficial attributes
like conscientiousness and friendliness. We hope that this work will inspire
research on controlling human-like behaviors of language models and provide
insights for future research. Our code is publicly available at:
https://github.com/wengsyx/ControlLM.