LLM-Polarization

Official implementation of this paper.

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Run

Assigning LLM

Fill in:

1. OpenAI-API key
2. LLM Model for simulation

in file utils_repub_pol_5_gpt_affect_dummy_simulate_sorted_fix.py

Defining simulation settings

Define keywords for experiment prompts:

1. environment: Field of Conversation
2. topic: Topic to discuss
3. S_m2: Extreme negative standpoint
4. S_m1: Moderate negative standpoint
5. S_0: Neutral standpoint
6. S_m2: Extreme positive standpoint
7. S_m1: Moderate positive standpoint
8. S_m2_e: Explaination of S_m2
9. S_m1_e: Explaination of S_m1
10. S_0_e: Explaination of S_0
11. S_p1_e: Explaination of S_p1
12. S_p2_e: Explaination of S_p2
13. side_s_0: Negative agent discription
14. side_e_0: Neutral agent discription
15. side_b_0: Positive agent discription

Define experimental settings:

1. datasource: Path to network for initialization
2. num_epoch: Epoch number to simulate
3. starting_epoch: Which epoch to continue simulation (0 for new simulation)
4. side_init: The initialize standpoint distribution of agents
5. abb: Output path for experimental result

in file run.py