LLM Failure Modes Vocabulary

generating plausible-sounding but factually incorrect or fabricated information

“The model hallucinated a citation to a paper that doesn't exist.”

over-agreeing with users and telling them what they want to hear rather than the truth

“Sycophancy made the model validate the user's incorrect assumption instead of correcting it.”

filling gaps in knowledge with plausible but invented details

“Unable to recall the actual date, the model confabulated a specific but wrong answer.”

gradually deviating from initial instructions over long conversations

“Instruction drift caused the formal tone to become casual after many exchanges.”

converging to repetitive or generic outputs regardless of varied inputs

“Mode collapse made every creative writing request produce similar clichéd stories.”

losing previously learned capabilities when trained on new data

“Fine-tuning on legal texts caused catastrophic forgetting of medical knowledge.”

getting stuck generating the same phrase or pattern repeatedly

“A repetition loop made the model output 'the the the' indefinitely.”

exceeding the model's context window, causing earlier content to be lost

“Context overflow made the model forget the original task instructions.”

subtle shifts in meaning of key terms through a conversation

“Semantic drift changed what 'the system' referred to mid-discussion.”

expressing certainty beyond what the model's actual knowledge warrants

“The model's overconfidence made its incorrect answer sound authoritative.”