MAWPS (Math Word Problem Repository) is the unified testbed for evaluating arithmetic word problem solvers — aggregating multiple elementary math datasets (AddSub, MultiArith, SingleOp, SingleEq) into a standardized repository that enabled systematic comparison of semantic parsing, neural seq2seq, and symbolic AI approaches to math reasoning.

What Is MAWPS?

• Scale: ~3,320 elementary school math word problems across multiple sub-datasets.
• Operations: Single and multi-step arithmetic — addition, subtraction, multiplication, division.
• Difficulty: Grade school level (ages 6-12); no algebraic variables, no competition-level insight required.
• Format: Natural language problem statement → numeric answer.
• Sub-datasets Included:
• AddSub: Single-step addition and subtraction (395 problems).
• MultiArith: Multi-step problems requiring multiple operations (600 problems).
• SingleOp: One-operation problems from diverse sources (562 problems).
• SingleEq: Single-equation problems with one unknown (508 problems).

The Semantic Parsing Tradition

MAWPS was created in an era when the dominant approach to math word problems was semantic parsing — converting text into formal representations:

• Template Mapping: "John has X apples and gives Y to Mary. How many does John have?" → X - Y = ?
• Equation Trees: Represent the solution as a tree of arithmetic operations.
• Parse + Execute: Translate text to equation, then evaluate the equation.

The repository unified these approaches by providing standardized train/test splits across all sub-datasets, enabling direct comparison.

Why MAWPS Was Strategically Important

• Baseline Establishment: Before MAWPS, each paper used different datasets with incompatible splits. MAWPS created a common ground for comparison.
• Saturation Demonstration: By 2020-2022, neural models (fine-tuned BERT, GPT-3) achieved ~95%+ accuracy on MAWPS — demonstrating that elementary arithmetic is essentially "solved" for LLMs.
• Stepping Stone: MAWPS→GSM8K→MATH represents a progression — MAWPS confirmed arithmetic capability, motivating harder benchmarks.
• Neural vs. Symbolic: MAWPS was a key arena for comparing end-to-end neural approaches (seq2seq) against symbolic semantic parsers — neural won by a significant margin for simple problems.

Performance by Model Generation

MAWPS in the Current Context

As a near-solved benchmark, MAWPS serves specific purposes:

• Regression Testing: Verify that new models do not lose basic arithmetic capability.
• Cross-lingual Transfer: Translate MAWPS into other languages to measure arithmetic transfer without algebraic complexity.
• Few-Shot Lower Bound: Measure how few examples a model needs to correctly solve grade-school arithmetic — tests sample efficiency.
• Error Analysis: The remaining ~2-5% errors reveal systematic failure modes (negative numbers, implicit unit conversions, ambiguous plurals).

Common Failure Patterns

• Implicit Units: "John bought 3 dozen eggs." Models sometimes fail to multiply by 12.
• Comparison to Reference: "Mary has 5 more apples than John, who has 8." Requires tracking two quantities.
• Multi-step Chaining: 4+ operation problems in MultiArith expose breakdown in intermediate result tracking.

Relationship to Other Benchmarks

MAWPS is the elementary math class benchmark — historically essential for establishing arithmetic NLP baselines, now primarily serving as a sanity check confirming that modern LLMs have thoroughly mastered grade-school arithmetic word problems.