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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Numeracy in Large Language Models: Fundamental Limitations and Paths to Improvement
TL;DR
Large language models (LLMs) struggle with basic numerical tasks despite strong performance in mathematical reasoning. The proposed Numerical Grounding Framework (NGF) identifies key areas for improvement in LLM numeracy.
✦ Why It Matters
Consider implementing supervised fine-tuning or external tools to improve numerical accuracy in your LLM applications.
Key Takeaways
- Explore digit-aware tokenization for improved model training.
- Utilize supervised fine-tuning for pretrained models.
- Incorporate external tools to enhance numerical tasks.
- Evaluate model performance using benchmarks like NumericBench.
How It Works
The Numerical Grounding Framework (NGF) decomposes numeracy into Representational Grounding (RG) and Procedural Grounding (PG). RG focuses on mapping numeral forms to their corresponding values and magnitudes, while PG ensures that arithmetic operations are executed according to mathematical definitions.
This structured approach allows for a clearer understanding of where LLMs fail in numerical tasks and how to address these shortcomings.