offloading-computation-generated-code-interpreter-removes-most-arithmetic CLM-0108mechanismsingle paperReviewed by AI and Russ Miller
Offloading the computation to generated code and an interpreter removes most arithmetic errors on math word problems.
Capability: Digit-level arithmetic
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- Offloading the computation to generated code and an interpreter removes most arithmetic errors on math word problems.
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Related claims
- Offloading the calculation to generated code removes the arithmetic as a source of error but does not remove error — what remains is the translation from problem to program, which is now the only thing the model is doing. Verification has to move there with it.Digit-level arithmetic
- Introduced grade-school math word problems on which 2021 models made frequent arithmetic slips, motivating verifier training.Digit-level arithmetic
- On clinical-calculator tasks where the formula, gold variables and full note are all supplied, having an open-weight model write case-specific Python for a restricted executor instead of doing the arithmetic itself gives no reliable gain at 7B (+3.29 pp, calculator-cluster CI crossing zero) but a clear gain at 32B (+7.05 pp, CI clear of zero), and the remaining errors are invented unit conversions and one-tier slips in banded scores rather than raw computation.Digit-level arithmetic · reviewed by AI
- Writing out reasoning steps improves how a problem is decomposed but does not fix the arithmetic inside a step — the model can decompose correctly and still get the calculation wrong, at the same confident register as when it is right. For exact arithmetic the fix is to move the computation out of the text, not to add more steps.Digit-level arithmetic · contested
- 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