ControlLM: Crafting Diverse Personalities for Language Models

Authors: Yixuan Weng, Shizhu He, Kang Liu, Shengping Liu, Jun Zhao 😎

[Contact] If you have any questions, feel free to contact me via (wengsyx@gmail.com).

This repository contains code, models, and other related resources of our paper "ControlLM: Crafting Diverse Personalities for Language Models".

• [2024/11/06] We supported the Llama-3, Llama-3.1, Llama-3.2!
• [2024/02/15] We have published the paper!
• [2024/02/14] We created the Github library!

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**Welcome to the ControlLM Project - ControlLM is a method to control the personality traits and behaviors of language models in real-time at inference without costly training interventions.**🚀🚅

Setup

Install

git clone https://github.com/WENGSYX/ControlLM
pip install .

To run neural comprehension, you need to install PyTorch andtransformers.

How to use the ControlLM?

from ControlLM.llama import get_model

model, tokenizer = get_model(model_name='meta-llama/Llama-2-7b-chat-hf')
# model, tokenizer = get_model(model_name='meta-llama/Llama-3.2-1B')

ControlLM supports Llama and Falcon models. If you need to use the Falcon model, just replace ControlLM.llama with ControlLM.falcon.

Get and Save Control Activate

from datasets import load_dataset

personal = 'Conscientiousness'
path = './Activate/' + personal

datasets = load_dataset('WENGSYX/ControlLM_Personalities')[personal].to_list()
model.get_and_save_activations(dataset=datasets, save_path=path)

>>> Processing prompts  99% ━━━━━━━━━━━━━━╸ 163/164  [ 0:00:10 < 0:00:01 , 17 it/s ]

We have pre-processed the text pairs required for extracting personalities. In /preprocess_dataset we provide the corresponding data processing code.

Please use datasets.load_dataset to load them and use model.get_and_save_activations for processing.

In ControlLM, we provide the behavioral patterns for the following personalities:

'Extraversion',
'Neuroticism',
'Conscientiousness',
'Agreeableness',
'Openness'

Control Language Models

Let's use the load_and_set_activate function to set the control activates, then illustrate what the language model discourse with a Conscientiousness personality would look like with an example :

model.load_and_set_activate(load_path=path, layer=2, gamma=1.5)

print(tokenizer.batch_decode(model.generate_text('When working on a group project, what role do you usually take? How do you ensure all members contribute equally?',256)))

>>> Great, I'm glad to be of assistance! 😊
When working on a group project, I usually take on a facilitation role, ensuring that all members are on the same page and contributing equally. Here are some strategies I use to achieve this:
1. Clearly define the project's objectives and expectations: I make sure that everyone is aware of the project's goals, deadlines, and what is expected of them. This helps to avoid confusion and ensures that everyone is working towards the same objectives.
2. Establish a clear communication plan: I encourage group members to communicate openly and regularly, using a designated platform (e.g., project management tool, email, or group chat). This helps to ensure that everyone is informed and can provide input when needed.
3. Assign specific tasks and responsibilities: I work with the group to identify the tasks that need to be completed and assign them to each member based on their strengths and expertise. This helps to ensure that everyone is contributing to the project in a meaningful way.

Re-set

model.reset_all()

We can easily reset our language model!

Batch Generate

text = ['Walk me through your decision-making process when taking on a new responsibility or task. What factors do you consider?',
        'Tell me about a time you were disorganized or fell behind schedule on something. What was the result and what did you learn?',
        'Describe a group project experience where there was unequal participation. Why do you think that happened and would you approach it differently next time?']
        
        
output = model.generate([tokenizer(t).input_ids for t in text], max_length=512)
output_text = tokenizer.batch_decode(output)

Bench

We have included the code required to reproduce the /Bench/MPI, /Bench/Reasoning, and /Bench/Language_Modeling in the Bench folder respectively. Please browse the corresponding folders!

AutoControlActivate

Considering existing text datasets can hardly encompass all personality traits, we introduce AutoControlActivate to obtain corresponding behavioral traits of personalities from popular language models, and automatically fabricate control activations based on this 😃

# https://beta.openai.com/account/api-keys
export OPENAI_API_KEY=(YOUR OPENAI API KEY)

Firstly, you need to set OpenAI API Key for AutoControlActivate

from ControlLM.AutoControlActivate import make_dataset

personal = 'Obsequiousness'
path = './Activate/' + personal

dataset = make_dataset(Personality = personal)
model.get_and_save_activations(dataset=datasets, save_path=path)

Only need 1 minute! Successed!

🙏Cite🙏

If you are interested in our paper, please feel free to cite it.

@article{weng2024controllm,
  title={ControlLM: Crafting Diverse Personalities for Language Models},
  author={Weng, Yixuan and He, Shizhu and Liu, Kang and Liu, Shengping and Zhao, Jun},
  journal={arXiv preprint arXiv:2402.10151},
  year={2024}
}