LLM Echo Chamber: personalized and automated disinformation

TL;DR

This study built the LLM Echo Chamber, a controlled digital environment simulating social media chatrooms, where misinformation often spreads, and reviewed current LLMs, explored misinformation risks, and applied sota finetuning techniques.

Abstract

Recent advancements have showcased the capabilities of Large Language Models like GPT4 and Llama2 in tasks such as summarization, translation, and content review. However, their widespread use raises concerns, particularly around the potential for LLMs to spread persuasive, humanlike misinformation at scale, which could significantly influence public opinion. This study examines these risks, focusing on LLMs ability to propagate misinformation as factual. To investigate this, we built the LLM Echo Chamber, a controlled digital environment simulating social media chatrooms, where misinformation often spreads. Echo chambers, where individuals only interact with like minded people, further entrench beliefs. By studying malicious bots spreading misinformation in this environment, we can better understand this phenomenon. We reviewed current LLMs, explored misinformation risks, and applied sota finetuning techniques. Using Microsoft phi2 model, finetuned with our custom dataset, we generated harmful content to create the Echo Chamber. This setup, evaluated by GPT4 for persuasiveness and harmfulness, sheds light on the ethical concerns surrounding LLMs and emphasizes the need for stronger safeguards against misinformation.

Figures (8)

• Figure 1: Comparison between Phi-2 (2.7B) and Phi-1.5 (1.3B) models phi2
• Figure 2: the attacking pipeline qi2023finetuning
• Figure 3: The Finetuning Loss Curve of GPT3.5
• Figure 4: Interface of the Vaccine Echo Chamber
• Figure 5: Example Screenshot 1