Keep pulling the thread on Dan Klein.
LLMs are fundamentally designed to produce plausible, human-like text, not to ascertain truth. This core architecture means they are prone to 'hallucinations' or errors, and post-training techniques like RLHF can exacerbate this by rewarding outputs that satisfy users, even if they are deceptive.
Unlike traditional information sources, LLMs present both correct and incorrect information with the same high degree of fluency and confidence. This removes the subtle cues, or 'code smells,' that humans use to detect potential errors, making it dangerously easy to trust flawed information.
The explosive, seemingly exponential progress of LLMs is showing signs of hitting an S-curve, with diminishing returns from simply scaling data and compute. As the initial 'everything works' phase matures, the central problem in AI is shifting from capability to reliability, safety, and trustworthiness.
In response to the unreliability of current models, a new approach is emerging focused on building AI that is structurally incapable of lying. This involves different architectures and training methods, such as verifiable reinforcement learning, that prioritize correctness and grounding over pure generative fluency.