Enigme: Generative Text Puzzles for Evaluating Reasoning in Language Models
John Hawkins·May 08, 2025
Summary
Transformer-decoder models, pivotal in AI, face reasoning challenges. Enigme, an open-source library, creates text-based puzzles to assess and improve these models' reasoning skills, targeting general-purpose abstract reasoning. Techniques like few-shot and chain-of-thought prompting aid in extracting reasoning for various tasks, including medical applications. Recent studies highlight models' rule learning, logical reasoning, token bias, mathematical reasoning, and inductive biases for higher-level cognition. Key papers explore these areas, aiming to enhance model capabilities.
Introduction
Background
Overview of Transformer-decoder models in AI
Objective
To explore reasoning challenges faced by Transformer-decoder models and the role of Enigme in assessing and improving these models' reasoning skills
Assessing Reasoning Skills with Enigme
Enigme: An Open-Source Library
Purpose and functionality of Enigme
Text-Based Puzzles
Types of puzzles used for assessment
General-Purpose Abstract Reasoning
Importance and application in various domains
Enhancing Reasoning through Prompting Techniques
Few-Shot Prompting
Explanation and benefits
Chain-of-Thought Prompting
Methodology and effectiveness
Extracting Reasoning for Specific Tasks
Medical applications and beyond
Recent Studies on Transformer Models
Rule Learning
Understanding and implications
Logical Reasoning
Techniques and advancements
Token Bias
Identification and mitigation strategies
Mathematical Reasoning
Capabilities and limitations
Inductive Biases for Higher-Level Cognition
Role and impact on reasoning
Key Papers and Research Contributions
Rule Learning in Transformers
Overview of studies and findings
Logical Reasoning Enhancements
Recent developments and methodologies
Addressing Token Bias
Strategies and outcomes
Mathematical Reasoning Capabilities
Advances and future directions
Inductive Biases for Higher-Level Cognition
Theoretical insights and practical implications
Conclusion
Summary of Findings
Future Directions
Ongoing research and potential areas for improvement
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does the Enigme library utilize transformer-decoder models to improve reasoning skills?
What are the novel contributions of recent studies in enhancing logical reasoning and rule learning in AI models?
What techniques are employed by Enigme to enhance reasoning in transformer-decoder models?
In what ways can few-shot and chain-of-thought prompting be integrated into existing AI models for improved reasoning?