The AI Politician project develops fine-tuned language models that accurately simulate political figures' communication styles, rhetorical patterns, and policy positions through parameter-efficient adaptation. Our system enables natural dialogue interactions, structured debates, and fact-grounded responses using retrieval-augmented generation.
Political discourse simulation presents unique challenges for language models due to the need for accurate representation of individual communication styles, consistent policy positions, and factual grounding. In this research, we introduce AI Politician—a system of fine-tuned Mistral-7B language models designed to recreate political discourse. We employ LoRA adapters and Retrieval-Augmented Generation (RAG) to enable efficient adaptation while maintaining factual accuracy. Our methodology combines parameter-efficient fine-tuning on political speech corpora with context-aware knowledge retrieval to produce coherent, personalized responses that maintain the distinct communication characteristics of specific political figures. We demonstrate the system's capabilities through one-on-one interactions, moderated debates, and issue-specific policy discussions, highlighting both the potential applications and ethical considerations of political AI simulations.
The AI Politician system integrates multiple components to deliver personalized political discourse. The workflow begins with (a) collecting and preprocessing political speech data, (b) applying LoRA fine-tuning to the Mistral-7B models and building the knowledge vector database, and (c) orchestrating response generation through query analysis, context retrieval, and inference.
Our approach consists of three integrated components: a personalized dialogue system, a debate management framework, and a contextual knowledge retrieval mechanism.
The foundation of our system is the Mistral-7B-Instruct-v0.2 large language model. To adapt this model for simulating specific political figures, we employ parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA). This approach enables the capture of nuanced individual communication styles and rhetorical patterns while substantially reducing the computational overhead associated with full fine-tuning. Specifically, our LoRA implementation utilizes adapters configured with a rank (r) of 8, a scaling factor (alpha) of 16, and a dropout rate of 0.05. These adapters are integrated into the Transformer architecture by targeting the query (`q_proj`), key (`k_proj`), value (`v_proj`), and output (`o_proj`) projection layers within the self-attention mechanism, as well as the gate (`gate_proj`), up (`up_proj`), and down (`down_proj`) projection layers within the feed-forward network components.
We developed a specialized data collection pipeline to gather high-quality training data from various sources:
All collected data underwent rigorous preprocessing, including deduplication, quality filtering, and format standardization before being used for fine-tuning.
Our RAG implementation uses a vector database containing policy statements, biographical information, and historical records. The retrieval process employs:
Explore our fine-tuned models. Interact directly via Hugging Face adapters or run locally with Ollama.
Fine-tuned to capture the distinctive communication style and rhetoric of Donald Trump.
Fine-tuned to capture the measured cadence and characteristic expressions of Joe Biden.
The AI Politician project demonstrates the viability of creating specialized language models capable of faithfully simulating political discourse. Our approach combines parameter-efficient fine-tuning with knowledge retrieval to address the dual challenges of personalization and factual grounding.
While our current implementation focuses on two high-profile political figures, the methodology is extensible to a broader range of politicians and public figures. Future work will explore:
We acknowledge the ethical considerations surrounding political AI simulations, including the potential for misuse and the importance of clear attribution. Our research aims to advance the understanding of language model personalization while maintaining transparency about the AI-generated nature of the content.
@misc{hill2024aipolitician,
author = {Hill, Natalie and Jones, Preston},
title = {{AI Politician: Fine-tuned Models for Political Discourse}},
year = {2024},
howpublished = {\url{https://aipolitician.org}},
note = {Project page available online. Code: https://github.com/SecretLabOU/aipolitician, Models: https://huggingface.co/collections/nnat03/ai-politician-680852b5f01da6e491803cdf}
}