Will AI cause a political interregnum
AI Large Language Model Training: The Potential Risks of Ideological Skewing — PSG Consulting
LLMs (AI Large Language Models) have become part of everyday life. Systems such as ChatGPT, Claude, Gemini, Meta AI (Llama) and X.ai's Grok handle billions of interactions daily. They increasingly shape what information people encounter and in what order, subtly deciding what's important and even what is true, sometimes without users realizing it. Because LLMs wield growing power over information exposure, it is vital to recognize the political and ideological structures at multiple stages of their design, and to identify manipulation risks.
Are LLMs Stifling Political Speech? An Assessment of How AI Models Protect Free Expression | Oversight Board
The Oversight Board’s first evaluation of large language models (LLMs) shows that some of the world’s most-used models from Anthropic, DeepSeek, Google, Meta
Sakana AI on Twitter / X
We’re excited to introduce Text-to-LoRA: a Hypernetwork that generates task-specific LLM adapters (LoRAs) based on a text description of the task. Catch our presentation at #ICML2025!Paper: https://t.co/2FRiVF1UXJCode: https://t.co/rx4G7dq1SWBiological systems are capable of… pic.twitter.com/UdUYfqRXBS— Sakana AI (@SakanaAILabs) June 12, 2025
We Don't Understand Neural Networks At The Algorithmic Level
The largest ongoing debate about AI is “Are Large Language Models (LLMs) intelligent?” That makes sense, at least: the evidence is ambiguous and the stakes a...
Import AI 446: Nuclear LLMs; China's big AI benchmark; measurement and AI policy
Will AIs be jealous of one another?
Brandon Stewart on Twitter / X
1/ New @Nature! We study how powerful institutions shape the information environment for LLMs. Commercial LLM training is opaque, so we trace a path from state-coordinated media -> training data -> model responses. pic.twitter.com/5LdFvzbFaf— Brandon Stewart (@b_m_stewart) May 13, 2026
Forcing Generative Models to Degenerate Ones: The Power of Data...
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious actors can...
Have we been measuring AI political bias wrong? A better approach is possible.
Why ideological preferences and epistemic failure in LLMs are not the same thing — and why the difference matters
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
There's Something Fundamentally Wrong With LLMs
LLMs aren't trained on the "vast majority of speech," experts warn, a major blind spot that could have sweeping consequences.
Pravda in the pipeline: Early evidence of state-adjacent propaganda in AI training data
An audit of Common Crawl reveals presence of Pravda and Glassbridge content in popular archive service, with further signs of LLM ingestion.
Curated retrieval versus open web search in public AI information...
Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these...
Generative AI for Pro-Democracy Platforms · From Novel Chemicals to Opera
Online discourse faces challenges in facilitating substantive and productive political conversations. Recent technologies have explored the potential of generative AI to promote civil discourse, encourage the development of mutual understanding in a discussion, produce . . .
Large language models are not the problem
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.
Large language model
A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots.[1] Biased or inaccurate training data can make an LLM's output less reliable.[2]