AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
AutoLogi generates logic puzzles with program verification and controllable difficulty, evaluating LLM reasoning from 35% to 73%.
Key Findings
Methodology
AutoLogi generates open-ended logic puzzles through a three-stage process: information extraction, program-based verifier generation, and difficulty-balanced data augmentation. Advanced LLMs are used to generate format requirements and verifiers, ensuring each problem has at least one solution. Cross-validation improves data accuracy.
Key Results
- On the AutoLogi benchmark, eight modern LLMs showed performance ranging from 35% to 73%, compared to 21% to 37% on traditional multiple-choice datasets, better reflecting true model capabilities.
- Training data generated by AutoLogi improved Qwen's performance on LiveBench from 30% to 35% at 7B scale and from 46% to 52% at 72B scale.
- AutoLogi's bilingual evaluation shows models perform similarly in both English and Chinese, indicating cross-lingual capabilities.
Significance
AutoLogi addresses the overestimation of model performance due to random guessing in multiple-choice formats by generating open-ended logic puzzles. Its program verification mechanism ensures problem accuracy and controllable difficulty, providing a more reliable evaluation of model reasoning abilities. This approach not only improves evaluation accuracy but also offers a new avenue for generating high-quality training data, systematically enhancing LLMs' reasoning capabilities.
Technical Contribution
AutoLogi achieves automated generation of open-ended logic puzzles through program verification and difficulty control, significantly enhancing evaluation reliability and discrimination. Its data augmentation method generates difficulty-balanced evaluation datasets by adjusting logical constraints, providing a more challenging test environment for model reasoning abilities.
Novelty
AutoLogi is the first to introduce program verification into logic puzzle generation, offering a more challenging evaluation method than traditional multiple-choice questions. Its bilingual benchmark and automated data augmentation method are pioneering in logical reasoning evaluation.
Limitations
- AutoLogi-generated logic puzzles may be overly complex in some cases, making them difficult for models to solve.
- The complexity of program verification increases computational costs, potentially limiting large-scale application.
Future Work
Future work could explore more efficient program verification methods to reduce computational costs. Additionally, further optimization of data augmentation strategies could generate more diverse and challenging logic puzzles.
AI Executive Summary
AutoLogi addresses the issue of performance overestimation in multiple-choice evaluations due to random guessing by generating open-ended logic puzzles. Its method includes information extraction, program-based verifier generation, and difficulty-balanced data augmentation, providing a more reliable evaluation of model reasoning abilities.
Through program verification, AutoLogi ensures each problem has at least one solution, avoiding the limitations of traditional evaluation methods. Experimental results show that AutoLogi has significant advantages in assessing model reasoning capabilities, with a wider performance range that better reflects true model abilities.
AutoLogi not only excels in evaluation but also offers a new avenue for generating high-quality training data. By using rejection sampling and program verification, it generates high-quality training data, significantly improving model performance on independent reasoning benchmarks. Future work will continue to optimize program verification and data augmentation methods to further enhance evaluation accuracy and challenge.
Deep Analysis
Background
Logical reasoning ability is a crucial evaluation metric for large language models. However, existing evaluation methods primarily rely on multiple-choice questions, which are susceptible to random guessing, leading to overestimation of model performance. New evaluation methods are needed to more accurately assess model reasoning capabilities.
Core Problem
Existing logical reasoning evaluation methods suffer from issues of random guessing and insufficient difficulty, leading to overestimation of model reasoning abilities. Designing an evaluation method that accurately reflects true model capabilities is a core challenge.
Innovation
AutoLogi achieves automated generation of open-ended logic puzzles through program verification and difficulty control. Its innovations include: 1) ensuring problem accuracy through program verification; 2) achieving difficulty balance through data augmentation; 3) providing bilingual evaluation, enhancing comprehensive assessment.
Methodology
- �� Information Extraction: Extract background information and logical constraints from corpora.
- �� Program-based Verifier Generation: Use LLMs to generate format requirements and verifiers, ensuring each problem has at least one solution.
- �� Data Augmentation: Generate problems of varying difficulty by adding or removing constraints.
Experiments
Experiments involved eight modern LLMs, including GPT-4, Claude, Qwen, and LLaMA. Evaluation datasets included the AutoLogi benchmark and original multiple-choice datasets. Multiple independent runs ensured result reliability.
Results
The AutoLogi benchmark showed model performance ranging from 35% to 73%, compared to 21% to 37% on traditional multiple-choice datasets, better reflecting true model capabilities. The data augmentation method improved evaluation discrimination.
Applications
AutoLogi can be used to evaluate the logical reasoning abilities of large language models, suitable for scenarios requiring high accuracy and challenge, such as intelligent assistants and automated decision-making systems.
Limitations & Outlook
AutoLogi's program verification increases computational costs, potentially limiting large-scale application. Future work will explore more efficient verification methods and optimize data augmentation strategies.
Plain Language Accessible to non-experts
Imagine you're playing a complex puzzle game. Each puzzle piece has different shapes and colors, and you need to fit them together according to the rules. AutoLogi is like a smart assistant that not only helps you choose the right pieces but also adjusts the game's difficulty based on your level. This way, it can better assess your problem-solving skills, rather than simply letting you guess from a few options.
ELI14 Explained like you're 14
Imagine you're playing a super tough puzzle game. This game isn't like ordinary multiple-choice questions; it requires you to think and solve problems on your own. AutoLogi is like a smart game designer that adjusts the puzzle's difficulty based on your level, helping you improve through challenges. This way, it can more accurately assess your reasoning skills, rather than letting you rely on luck to guess the answers.
Glossary
AutoLogi
AutoLogi is a system for automatically generating logic puzzles to evaluate the reasoning abilities of large language models.
Used in the paper as a tool for generating open-ended logic puzzles.
Program Verification
Program verification is a technique for checking the correctness of problem solutions through program code.
Used to ensure that AutoLogi-generated problems have at least one solution.
Rejection Sampling
Rejection sampling is a technique for filtering out correct answers using verifiers.
Used to generate high-quality training data.
Bilingual Benchmark
A bilingual benchmark includes evaluation data in both English and Chinese.
Used to evaluate cross-lingual reasoning capabilities of models.
Data Augmentation
Data augmentation is a method of generating problems of varying difficulty by adding or removing constraints.
Used to generate a difficulty-balanced evaluation dataset.
Open Questions Unanswered questions from this research
- 1 How to improve the efficiency of program verification without increasing computational costs?
- 2 How to further optimize data augmentation strategies to generate more diverse problems?
Applications
Immediate Applications
Intelligent Assistant Evaluation
Use AutoLogi to evaluate the logical reasoning abilities of intelligent assistants, ensuring their performance in complex tasks.
Long-term Vision
Automated Decision Systems
Enhance the reasoning capabilities of automated decision systems using data generated by AutoLogi, achieving smarter decision-making.
Abstract
While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are vulnerable to random guessing, leading to overestimated performance and substantial performance fluctuations. To obtain more accurate assessments of models' reasoning capabilities, we propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi. Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models' reasoning abilities. Extensive evaluation of eight modern LLMs shows that AutoLogi can better reflect true model capabilities, with performance scores spanning from 35% to 73% compared to the narrower range of 21% to 37% on the source multiple-choice dataset. Beyond benchmark creation, this synthesis method can generate high-quality training data by incorporating program verifiers into the rejection sampling process, enabling systematic enhancement of LLMs' reasoning capabilities across diverse datasets.