Human LLM Bullshitting: Humans confidently assert wrong information, from flat-earth beliefs to misremembered historical “facts” and fake news that spread through sheer conviction Hallucination: LLMs generate plausible but factually incorrect content, stating falsehoods with the same fluency as facts People-Pleasing: Humans optimize for social harmony at the expense of honesty, nodding along with the boss’s bad idea or validating a friend’s flawed logic to avoid conflict Sycophancy: LLMs trained with human feedback tell users what they want to hear, even confirming obviously wrong statements to avoid disagreement Zoning Out: Humans lose focus during the middle of meetings, remembering the opening and closing but losing the substance sandwiched between Lost in the Middle: LLMs perform well when key information appears at the start or end of input but miss crucial details positioned in the middle Overconfidence: Humans often feel most certain precisely when they’re least informed—a pattern psychologists have documented extensively in studies of overconfidence Poor Calibration: LLMs express high confidence even when wrong, with stated certainty poorly correlated with actual accuracy Trees for the Forest: Humans can understand each step of a tax form yet still get the final number catastrophically wrong, failing to chain simple steps into complex inference Compositional Reasoning Failure: LLMs fail multi-hop reasoning tasks even when they can answer each component question individually First Impressions: Humans remember the first and last candidates interviewed while the middle blurs together, judging by position rather than merit Position Bias: LLMs systematically favor content based on position—preferring first or last items in lists regardless of quality Tip-of-the-Tongue: Humans can recite the alphabet forward but stumble backward, or remember the route to a destination but get lost returning Reversal Curse: LLMs trained on “A is B” cannot infer “B is A”—knowing Tom Cruise’s mother is Mary Lee Pfeiffer but failing to answer who her son is Framing Effects: Humans give different answers depending on whether a procedure is framed as “90% survival rate” versus “10% mortality rate,” despite identical meaning Prompt Sensitivity: LLMs produce dramatically different outputs from minor, semantically irrelevant changes to prompt wording Rambling: Humans conflate length with thoroughness, trusting the thicker report and the longer meeting over concise alternatives Verbosity Bias: LLMs produce unnecessarily verbose responses and, when evaluating text, systematically prefer longer outputs regardless of quality Armchair Expertise: Humans hold forth on subjects they barely understand at dinner parties rather than simply saying “I don’t know” Knowledge Boundary Blindness: LLMs lack reliable awareness of what they know, generating confident fabrications rather than admitting ignorance Groupthink: Humans pass down cognitive biases through culture and education, with students absorbing their teachers’ bad habits Bias Amplification: LLMs exhibit amplified human cognitive biases including omission bias and framing effects, concentrating systematic errors from their training data Self-Serving Bias: Humans rate their own work more generously than external judges would, finding their own prose clearer and arguments more compelling Self-Enhancement Bias: LLMs favor outputs from themselves or similar models when evaluating responses Via Claude
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