Atom2.7m: Representation-Level Specialization for Arithmetic-Aware Small Language Models

Atom2.7m is a small, 2.74M-parameter language model designed to improve arithmetic performance by incorporating arithmetic-aware numeric representation, achieving 69.24% accuracy on the ArithMark2.0 benchmark. Despite being much smaller than other models like GPT-2 XL, it outperforms them by explicitly exposing digit order, place value, and operand roles to the model, addressing representation-level failures common in larger models. Unlike conventional models that struggle with arithmetic due to tokenization and positional embedding issues, Atom2.7m leverages structured representations to make arithmetic operations clearer and more efficient. The model demonstrates that specialized representations can enhance arithmetic capabilities without relying solely on scaling, suggesting that structured tasks benefit significantly from structured representations. While Atom2.7m integrates BPE-style text handling with numeric structure, it remains a specialized model with limited general-language ability, indicating potential for further exploration in representation-level specialization for other exact, structured domains.

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