When Models Look Back: Memory, Leakage, and the Quiet Failure Modes of LLM Training
Opening — Why this matters now Large language models are getting better at many things—reasoning, coding, multi‑modal perception. But one capability remains quietly uncomfortable: remembering things they were never meant to remember. The paper underlying this article dissects memorization not as a moral failure or an anecdotal embarrassment, but as a structural property of modern LLM training. The uncomfortable conclusion is simple: memorization is not an edge case. It is a predictable outcome of how we scale data, objectives, and optimization. ...
