🧩 MachineSoM

Code for the paper "Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View".

• The Society of Mind (SoM): the emergence of intelligence from collaborative and communicative computational modules, enabling humans to collaborate and complete complex tasks effectively
• Societies of LLM agents with different traits: easy-going and overconfident
• Collaboration Processes: debate and self-reflection
• Interaction Strategies: when to interact, interact with whom

📬 News!

• [2023.10.03] The paper "Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View" is released.
• [2023.07.13] MachineSoM code is released!

🎉 Quick Links

• 🛠️ Requirements & Dataset
• 🚴 How to run

🛠️ Requirements & Dataset

Configure the environment using the following command:

conda create -n masom python=3.9
pip install -r requirements.txt

The data we sampled and used for the experiment is in the folder eval_data. You can download the raw datasets for MMLU, Math and Chess Move Validity separately.

🚴 How to run

Here is a brief overview of each file in the folder src:

# Core Code
|- api.py               # API for storing the experiments
|- dataloader.py        # Load datasets for experiments
|- evaluate.py          # Evaluate the results of the experiment
|- prompt.py            # Stores all the prompts involved in the experiment.
|- utils.py             # The management center of the agents.
|- run_main.py          # Simulate a society of agents using different collaborative strategies to solve problems (main experiment)
|- run_agent.py         # Exploring the Impact of Agent Quantity
|- run_strategy.py      # All agents can adopt different thinking patterns for collaboration

# Other Code
|- run_main.sh          # Main experiment running script
|- run_agent.sh         # The running script of the experiment on the number of agents
|- run_strategy.sh      # The running script of the experiment on the strategies
|- run_turn.sh          # The running script of the experiment on the number of collaboration rounds
|- conformity_and_consistency.py    # Process and draw figures such as Figure 6 and Figure 7 in the paper
|- draw                 # Include the drawing code for all the figures in the paper
    |- draw_10_agent.py
    |- draw_conformity
    |- ...

1. Edit src/api.py to add your api-key.

   openai_api = {
       "replicate":[
           "api_1", "api_2", 
       ],
       "dashscope": [
           "api_1"
       ],
       "openai": [
           "api_1"
       ],
       "anyscale":[
           "api_1", "api_2", "api_3"
       ]
   }

   Our coding framework offers compatibility with a variety of inference services across multiple platforms, such as Replicate, OpenAI, Dashscope, and Anyscale. Specifically, Dashscope facilitates the deployment of the Qwen model, whereas OpenAI provides support for the GPT model integration.

2. Execute the scripts run_main.sh, run_agent.sh, run_turn.sh, and run_strategy.sh housed in the src directory. These scripts are designed to initiate a variety of experiments: the main experiment (corresponding to Table 2 in the paper), variations in agent numbers (Figure 3 in the paper), differing collaboration round counts (Figure 4 in the paper), and trials involving alternative collaboration strategies (Figure 5 in the paper). You can adjust the parameters within the scripts to accommodate different experimental settings.

   All the data in the paper is available for download on Google Drive.

3. Execute the evaluate.py in the src directory.

   a. For the main experiment results, you can execute the following command:

   python evaluate.py main_table --experiment_type gpt-1106-main --dataset mmlu

   This code will be output in LaTeX code format. The argument --experiment should be the name of a folder. To replicate the results presented in the paper, after downloading and uncompressing it into the src root directory, rename the upload to results. At this point, the available options for the --experiment parameter are gpt-1106-main, llama13-main, llama70-main, qwen-main, and mixtral-main. The optional values for argument --dataset are mmlu, math, and chess.

   b. For the siginificant test, you can execute the following commands:

   python evaluate.py anova --types main --dataset chess --experiment_type "['llama13-main','gpt-1106-main']"
   python evaluate.py anova --types turn --dataset chess  --experiment_type "['llama13-turn-4','llama70-turn-4']"
   python evaluate.py anova --types 10-turn --dataset chess --experiment_type "['gpt-1106-turn-10', 'qwen-turn-10', 'mixtral-turn-10']"
   python evaluate.py anova --types agent --dataset chess --experiment_type "['llama13-main','llama70-main']"
   python evaluate.py anova --types 10-agent --dataset chess --experiment_type "['gpt-1106-main','qwen-main']"
   python evaluate.py anova --types strategy --dataset chess --experiment_type "['gpt-1106-main','qwen-main']"

   You can change the --dataset and --experiment_type to get the original result (e.g., Table 6 in the paper).

   c. We also provide code for drawing figures in the paper. For the vast majority of figures, the following code can be executed:

   python evaluate.py draw --types distribute --experiment_type gpt-1106-main
   python evaluate.py draw --types agent --experiment_type llama13-main
   python evaluate.py draw --types turn --experiment_type llama70-main
   python evaluate.py draw --types strategy --experiment_type gpt-1106-main
   python evaluate.py draw --types 10-agent --experiment_type gpt-1106-main
   python evaluate.py draw --types 10-turn --experiment_type gpt-1106-main --dataset chess
   python evaluate.py draw --types radar --experiment_type gpt-1106-main
   python evaluate.py draw --types 10-agent-consistent --experiment_type gpt-1106-main
   python evaluate.py draw --types word --experiment_type gpt-1106-main

   d. It should be noted that in order to obtain the results of Figures 6 and 7, you need to execute the following code:

   python conformity_and_consistency.py --experiment_type 'mixtral-main' --type 'consistent'
   python conformity_and_consistency.py --experiment_type 'mixtral-main' --type 'conformity'

   There are stylistic differences between the figures in the paper and those generated by the code, such as legend, color, layout, etc. But the data has not changed at all.