Debate: without strong verification, LLM approximation errors go silently unnoticed
gerardsans · x · 2026-09-19
A technical argument over LLM sampling and verification: the poster claims that without strong verification, models absorb the last tokens across the input space, so many silent approximation errors go unnoticed — and because outputs are conditioned on input over a huge output space, external verification is mandatory. The replier counters that these are fundamentals of next-token sampling, urging a review before misunderstandings snowball into catastrophic outcomes in high-stakes scenarios.
Related event: Researchers Debate Silent Error Accumulation in LLMs Without Verification(3 posts)→
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