Основні поняття
Leveraging autoformalization to improve LLM quantitative reasoning.
Анотація
This paper discusses the use of autoformalization to enhance large language models' (LLMs) ability to solve mathematical quantitative reasoning problems. By translating informal mathematical statements into formal languages, LLMs can be verified automatically for internal consistency, leading to improved accuracy in identifying correct answers. The method, known as Don't Trust: Verify (DTV), outperforms traditional methods like majority voting, showcasing consistent improvements across different datasets and model sizes.
Directory:
Introduction
LLMs' advancements in quantitative reasoning tasks.
Need for improved heuristics to identify correct answers.
Autoformalization
Translating informal mathematical statements into formal languages.
Leveraging LLMs' capabilities for autoformalization.
Verification Process
Using automated theorem provers to verify formal solutions.
Importance of internal consistency in formal reasoning.
Experiments
Evaluation on GSM8K, MATH, and MultiArith datasets.
Comparison with baselines and performance improvements.
Ablation Study
Impact of solution formalization and statement filters on performance.
Qualitative Analysis
Case studies showcasing successful formalization and verification.
Limitations and Future Work
Scope limitations of current formal theorem proving environments.
Potential improvements through reinforcement learning and effective filters.
Статистика
Large language models (LLMs) are becoming increasingly capable of solving mathematical quantitative reasoning problems.
Autoformalization can automatically reject solutions inconsistent with formalized versions.
DTV outperforms vanilla majority voting by more than 12% on GSM8K.
Цитати
"The product of the two numbers is the product of their LCM and their GCD: 3720 * 8 = 29760." - Informal Solution
"Since one of the numbers is 120, we can divide this product by 120 to obtain the other number: 29760 / 120 = 248." - Informal Solution