arithmetic-accuracy-frontier-models-depends-strongly-how-digits CLM-0023mechanismsingle paperReviewed by AI and Russ Miller
Arithmetic accuracy in frontier models depends strongly on how digits are chunked into tokens; a different digit grouping changes error rates.
Capability: Digit-level arithmetic
Sources
- Arithmetic accuracy in frontier models depends strongly on how digits are chunked into tokens; a different digit grouping changes error rates.
Either way, bring evidence: a link, or what you saw.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- Multi-digit multiplication accuracy falls to near zero as problem size grows, and models rely on pattern matching rather than a learned algorithm.Digit-level arithmetic
- When a text LLM is prompted to forecast numeric price series, feeding the history as integer-rounded values on the original price scale gives lower forecast error than full-decimal raw values, standard-normalized values, or per-window instance-normalized values, because rounding cuts token fragmentation while keeping magnitudes in the distribution the model saw in pretraining.Predicting future events · reviewed by AI
- When each capability domain's annual benchmark frontier is normalised to its own scale (0 at the benchmark's entry-year frontier, 100 at a perfect score), the domains move on different curves — broad knowledge gains slow from 32.8 to 26.9 to 17.6 points a year, legal reasoning from 48.2 to 11.4 to 4.9, while advanced mathematics accelerates from 32.8 to 53.6 and multimodal reasoning jumps 59.7 then only 2.5 — so a single aggregate benchmark score conceals both the level and the trajectory shape of progress, and cannot say which capabilities are saturating.Whether the measurement made the finding · reviewed by AI