Teaching Transformers to Understand Numbers (for Real)
Large language models can ace math benchmarks yet still stumble on simple number sense because they treat numbers like ordinary words. This work fixes that by giving models a value-aware way to read numbers.
How it works: whenever a number appears, the input is augmented with a tiny prefix token whose embedding is conditioned on the number’s actual magnitude. That injects value information directly into the model’s input space while keeping standard tokenizers and decoder-only Transformer architectures unchanged.
- Drop-in: no changes to tokenizers or model architecture.
- Versatile: handles different formats (integers, decimals, scientific notation) and longer operands.
- Effective: beats baselines on arithmetic tasks, improving basic numerical robustness.
Takeaway: when models see numbers as values—not just symbols—they make fewer math mistakes.
Paper: https://arxiv.org/abs/2601.09706
Paper: https://arxiv.org/abs/2601.09706v1
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