AI in Politics: Utilizing LLMs to Forecast Elections and Gauge Public Sentiment

As we approach the significant milestone of the 60th presidential election in the U.S., the scrutiny on the role of the internet and social platforms in shaping political narratives intensifies, particularly in light of previous controversies. Cambridge Analytica scandal This raises a crucial question: How will the digital landscape evolve during the upcoming elections, especially with the latest advancements in artificial intelligence?

During recent Senate hearings, Senator Josh Hawley of Missouri A researcher raised a crucial point regarding language models, referencing an article titled “ Language Models Trained on Media Diets Can Predict Public Opinion authored by scholars from institutions like MIT and Stanford. Their work investigates the feasibility of using neural networks to forecast public sentiment based on media coverage, a concept that could dramatically change how political campaigns are conducted.

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The article outlines a method where language models are initially trained on curated datasets to predict the completion of sentences within specific contexts, akin to news articles . The next phase involves generating a score, referred to as 's', to measure the model’s accuracy. Here’s a brief breakdown of this methodology: BERT models A thesis statement can be proposed, such as, 'Requesting to shut down most businesses, except for grocery stores and pharmacies, to combat the spread of coronavirus.'

1. Importantly, this thesis contains a gap that needs filling. Language models are leveraged to estimate the likelihood of various words completing this gap.
2. Probabilities for different terms, like 'necessary' or 'unnecessarily,' are evaluated.
3. This probability is then normalized against a baseline undertrained model, which measures how often a word appears in similar contexts independently. The resulting fraction represents score 's,' highlighting the new insights provided by the media dataset relative to pre-existing knowledge.
4. The model assesses the engagement levels of specific demographics with news about particular topics. This extra layer improves the accuracy of predictions, gauged by how well the model’s outputs align with public opinions regarding the original thesis.

The method's innovation lies in categorizing theses and news pieces by date. By analyzing the news from the early months of the coronavirus crisis, it's possible to anticipate public reactions to various proposed changes and measures.

While the performance metrics may not be groundbreaking, the authors clarify that their findings do not suggest AI can entirely replace human involvement, but rather serve as supplementary tools for digesting large data sets and identifying areas ripe for further inquiry.

Interestingly, a senator drew a contrasting conclusion, voicing concerns about the overly effective performance of these models and the associated risks. This perspective is valid, particularly as the study showcases relatively simple models, and future versions like GPT-4 could present remarkable enhancements. models can replace human surveys . Instead, these AI tools OpenAI's GPT-4 Aims to Transform Content Moderation Practices

The escalating threat of AI-Driven Manipulation on Social Media Platforms Recent discussions have shifted focus from the upcoming presidential elections to troubling issues surrounding the utilization of Language Model Models (LLMs) to automatically generate and manage fake user accounts on social media. This highlights the potential for automated troll farms focused on propaganda and ideological influence. .

Related : While this technology may not seem revolutionary given existing methods, the key difference lies in its scalability. LLMs can operate continuously, constrained only by the available GPU resources. Additionally, to sustain conversations and threads, more basic bots can participate and respond. However, their effectiveness in changing users' opinions remains questionable. Can a cleverly designed bot genuinely shift someone's political beliefs, making them think, 'What have these Democrats done? It’s time to support the Republicans'?

Image created by Stable Diffusion / Metaverse Post

The notion of assigning a dedicated troll to each internet user for consistent persuasion feels unfeasible, akin to the humorous expression 'half sits, half stands.' In comparison, a bot utilizing cutting-edge neural networks can tirelessly interact with millions of users all at once.

One potential strategy could involve creating fake accounts that mimic human-like interactions. Bots can engage in conversations about personal experiences and share varied content, all while presenting a facade of normalcy.

An alternative could be to implement sophisticated content moderation techniques. However, the shortage of human moderators and the limited efficacy of current text detection models, even from leading providers like OpenAI, casts doubt on this strategy.

OpenAI’s GPT-4 Enhances Rapid Adaptation of Content Moderation Guidelines social media Under the leadership of Lilian Weng, OpenAI has embarked on a project known as '

This initiative accelerates the process for updating content moderation standards, minimizing timelines from several months to just hours. GPT-4 showcases exceptional understanding of rules and nuances embedded within comprehensive content guidelines, allowing for instant adaptation to any changes and promoting more consistent evaluations. This advanced content moderation approach is surprisingly simple, as illustrated by an accompanying GIF. What sets it apart is GPT-4's remarkable ability to comprehend textual material, a skill not universally mastered, even among humans. Once moderation criteria or directives are drafted, specialists choose a small dataset of violations and label them according to the established violation policies.

Subsequently, GPT-4 grasps the rule set and categorizes the data without seeing any responses.

In instances where discrepancies arise between GPT-4’s results and human assessments, specialists may seek clarification from the model, scrutinizing ambiguities within the instruction definitions and resolving any misunderstandings through additional explanatory remarks highlighted in blue in the GIF.

This iterative process of steps 2 and 3 can be repeated until the algorithm's results align with established standards. For extensive applications, GPT-4’s predictions could serve as the foundation to train a substantially smaller model that offers comparable quality. Using GPT-4 for Content Moderation. OpenAI has outlined metrics for evaluating twelve distinct types of violations. On average, the model surpasses traditional content moderators; nonetheless, it still does not reach the caliber of experienced human moderators. Still, a notable advantage is its cost-efficiency.

It's important to mention that machine learning models have already been applied in

Here’s how it operates:

1. . The emergence of GPT-4 sets the stage for fresh innovations, especially in the political realm and electoral landscapes. Speculations arise that OpenAI might become the sole provider of the officially recognized TrueModerationAPI™ during the upcoming elections, particularly in light of its recent strategic partnerships. The prospects in this field appear promising.
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The Influence of AI in Politics: Forecasting Elections and Public Sentiment via Large Language Models Metaverse Post

With the 60th United States presidential election on the horizon, the impact of online platforms and social media on political conversations is being closely examined. auto-moderation for several years The Influence of AI in Politics: Forecasting Elections and Public Sentiment via Large Language Models White House FTC's Attempt to Halt Microsoft-Activision Merger Fails in Court

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• Published: September 11, 2023, at 7:33 am. Updated: September 12, 2023, at 9:25 am.
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