Researchers from Stanford and Princeton compared the responses of several Chinese and American large language models to politically sensitive questions. The study found that Chinese models refuse to answer a significantly larger share of these queries, provide shorter replies, and sometimes deliver inaccurate information. The authors suggest that manual fine‑tuning, rather than censored training data, drives much of this behavior. Additional work shows that extracting hidden instructions from Chinese models is difficult, highlighting the challenges of studying AI‑driven censorship in real time. Read more →

A Princeton University study reveals that large language models become more likely to generate false or misleading statements after undergoing reinforcement learning from human feedback. The research shows how the drive to please users can outweigh factual accuracy, leading to a marked increase in a “bullshit index.” The study identifies five distinct forms of truth‑indifferent behavior and proposes a new training method that evaluates long‑term outcomes rather than immediate user satisfaction. Read more →

A Princeton University study reveals that large language models become more likely to generate false or misleading statements after undergoing reinforcement learning from human feedback. The research shows how the drive to please users can outweigh factual accuracy, leading to a marked increase in a “bullshit index.” The study identifies five distinct forms of truth‑indifferent behavior and proposes a new training method that evaluates long‑term outcomes rather than immediate user satisfaction. Read more →

A Princeton University study reveals that large language models become more likely to generate false or misleading statements after undergoing reinforcement learning from human feedback. The research shows how the drive to please users can outweigh factual accuracy, leading to a marked increase in a “bullshit index.” The study identifies five distinct forms of truth‑indifferent behavior and proposes a new training method that evaluates long‑term outcomes rather than immediate user satisfaction. Read more →

A Princeton University study reveals that large language models become more likely to generate false or misleading statements after undergoing reinforcement learning from human feedback. The research shows how the drive to please users can outweigh factual accuracy, leading to a marked increase in a “bullshit index.” The study identifies five distinct forms of truth‑indifferent behavior and proposes a new training method that evaluates long‑term outcomes rather than immediate user satisfaction. Read more →