# The Effect of Scripts and Formats on LLM Numeracy

arXiv: https://arxiv.org/abs/2601.15251 · HTML: https://mlsanity.com/paper/2601.15251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://mlsanity.com/paper/1909.07940/) (2019)
- [Investigating the Limitations of Transformers with Simple Arithmetic Tasks](https://mlsanity.com/paper/2102.13019/) (2021)
- [Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs](https://mlsanity.com/paper/2402.14903/) (2024)
- [Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs](https://mlsanity.com/paper/2310.10358/) (2023)
- [The Lookahead Limitation: Why Multi-Operand Addition is Hard for LLMs](https://mlsanity.com/paper/2502.19981/) (2025)
- [Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results](https://mlsanity.com/paper/2511.05162/) (2025)
- [Efficient numeracy in language models through single-token number embeddings](https://mlsanity.com/paper/2510.06824/) (2025)
- [On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures](https://mlsanity.com/paper/2506.02591/) (2025)
- [Investigating the interaction of linguistic and mathematical reasoning in language models using multilingual number puzzles](https://mlsanity.com/paper/2506.13886/) (2025)
- [Linear models and linear mixed effects models in R with linguistic applications](https://mlsanity.com/paper/1308.5499/) (2013)

## Built on by (most cited first, 1 of 1)

- [What Makes Good Multilingual Reasoning? Disentangling Traces with Measurable Features](https://mlsanity.com/paper/2604.04720/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has been given to how these models perform when numerical expressions deviate from the prevailing conventions present in their training corpora. In this work, we investigate numerical reasoning across a wide range of numeral scripts and formats. We show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats, despite the underlying mathematical reasoning being identical. We further demonstrate that targeted prompting strategies, such as few-shot prompting and explicit numeral mapping, can greatly narrow this gap. Our findings highlight an overlooked challenge in multilingual numerical reasoning and provide actionable insights for working with LLMs to reliably interpret, manipulate, and generate numbers across diverse numeral scripts and formatting styles.