The Token Games: Evaluating Language Model Reasoning with Puzzle Duels
Token Games evaluates language model reasoning by having models create their own puzzles, saving costs.
Key Findings
Methodology
Token Games (TTG) is an evaluation framework where models challenge each other by creating programming puzzles. Each puzzle is represented by a function returning a boolean, and models find inputs to make it return True. Elo ratings are computed from duel results to compare model capabilities.
Key Results
- Result 1: TTG evaluated 10 frontier models, closely matching existing benchmarks like Humanity's Last Exam, with correlations as high as 0.87.
- Result 2: Creating high-quality puzzles remains challenging for current models, showcasing creativity and task creation abilities.
- Result 3: TTG runs cost less than $200, with no human involvement in puzzle creation.
Significance
TTG offers a novel reasoning evaluation method that avoids saturation issues of existing benchmarks. It not only assesses reasoning ability but also tests creativity and task creation, impacting academia and industry.
Technical Contribution
TTG evaluates by having models create puzzles, avoiding data contamination issues, offering new theoretical guarantees and engineering possibilities. It dynamically adapts to model capability improvements compared to traditional benchmarks.
Novelty
TTG is the first to use model-created puzzles for reasoning evaluation, differing from existing benchmarks that rely on human-designed questions.
Limitations
- Limitation 1: Models may be overconfident, proposing puzzles they cannot solve, leading to failures.
- Limitation 2: Current models struggle to create high-quality puzzles, affecting evaluation accuracy.
Future Work
Future work can explore improving models' puzzle creation abilities and extend TTG to evaluate other skills like emotional understanding and language generation.
AI Executive Summary
As language models improve, evaluating their reasoning capabilities becomes increasingly challenging. Existing benchmarks rely on human-designed hard questions, which are costly and prone to saturation. Token Games (TTG) offers an innovative solution by having models create their own puzzles for evaluation. Models take turns as proposers and solvers, creating and solving programming puzzles. Experiments show TTG rankings closely match existing benchmarks, with costs under $200. TTG assesses reasoning abilities and tests creativity and task creation, impacting academia and industry. However, models face challenges in creating high-quality puzzles, and future work will explore improving model capabilities.
Deep Analysis
Background
With models like GPT-3 and PaLM, language model reasoning capabilities have significantly improved. Early benchmarks like GSM8K and MATH have become saturated, and recent benchmarks like GPQA and HLE rely on expert-designed hard questions, which are costly.
Core Problem
Evaluating language model reasoning capabilities is a core problem. Existing benchmarks rely on human-designed questions, making it difficult to measure true model abilities and prone to data contamination.
Innovation
TTG's innovation lies in evaluating through model-created puzzles. It avoids data contamination issues, dynamically adapts to model capability improvements, and tests model creativity.
Methodology
- �� Models alternate as proposers and solvers
- �� Proposer creates programming puzzle and provides solution
- �� Solver attempts to solve puzzle
- �� Elo ratings computed from duel results
Experiments
Experiments conducted on 10 frontier models, with 10 rounds of duels per model pair. Elo rating system used to evaluate model performance, compared with existing benchmarks like HLE and ARC-AGI.
Results
TTG rankings closely match existing benchmarks, with correlations as high as 0.87. Models face challenges in creating high-quality puzzles, showcasing creativity and task creation abilities.
Applications
TTG can be used to evaluate reasoning and creativity, applicable to academic research and industry applications. It offers a low-cost evaluation method, avoiding the high costs of human-designed questions.
Limitations & Outlook
Models may be overconfident, proposing puzzles they cannot solve. Current models struggle to create high-quality puzzles, affecting evaluation accuracy. Future work will explore improving model capabilities.
Plain Language Accessible to non-experts
Imagine playing a brain game with a friend where you take turns creating and solving puzzles. Each puzzle is a small program, and you need to find inputs that make it return the correct answer. This game tests not only your problem-solving skills but also your creativity, as you need to design puzzles that your friend can't solve. Token Games is like this game, but played by language models. It helps us evaluate models' reasoning and creativity without needing human-designed puzzles.
ELI14 Explained like you're 14
Imagine playing a brain game with a friend where you take turns creating and solving puzzles. Each puzzle is a small program, and you need to find inputs that make it return the correct answer. This game tests not only your problem-solving skills but also your creativity, as you need to design puzzles that your friend can't solve. Token Games is like this game, but played by language models. It helps us evaluate models' reasoning and creativity without needing human-designed puzzles.
Glossary
Token Games (TTG)
An evaluation framework for assessing language model reasoning capabilities through model-created programming puzzles.
Used to evaluate model reasoning and creativity.
Programming Puzzle
A problem represented by a function returning a boolean, where models find inputs to make it return True.
Used to assess model problem-solving abilities.
Elo Rating
A scoring system for comparing model capabilities based on duel results.
Used to evaluate relative model abilities.
Creativity Test
Assessing models' ability to create new problems, avoiding repetition of training data problems.
Used to evaluate model creativity and task creation abilities.
Data Contamination
The presence of evaluation problems in training data, affecting evaluation accuracy.
TTG avoids data contamination by using model-created puzzles.
Open Questions Unanswered questions from this research
- 1 How to improve models' puzzle creation abilities remains to be further researched.
- 2 Models struggle to create high-quality puzzles, affecting evaluation accuracy.
Applications
Immediate Applications
Reasoning Evaluation
TTG can be used to assess models' reasoning capabilities, applicable to academic research and industry applications.
Creativity Test
Evaluates models' creativity through model-created puzzles, applicable to task creation ability research.
Long-term Vision
Dynamic Evaluation Framework
TTG offers a framework that dynamically adapts to model capability improvements, potentially extending to other skill evaluations in the future.
Abstract
Evaluating the reasoning capabilities of Large Language Models is increasingly challenging as models improve. Human curation of hard questions is highly expensive, especially in recent benchmarks using PhD-level domain knowledge to challenge the most capable models. Even then, there is always a concern about whether these questions test genuine reasoning or if similar problems have been seen during training. Here, we take inspiration from 16th-century mathematical duels to design The Token Games (TTG): an evaluation framework where models challenge each other by creating their own puzzles. We leverage the format of Programming Puzzles - given a function that returns a boolean, find inputs that make it return True - to flexibly represent problems and enable verifying solutions. Using results from pairwise duels, we then compute Elo ratings, allowing us to compare models relative to each other. We evaluate 10 frontier models on TTG, and closely match the ranking from existing benchmarks such as Humanity's Last Exam, spending less than $200 USD and without involving any human effort in creating puzzles. We also find that creating good puzzles is still a highly challenging task for current models. Overall, our work suggests new paradigms for evaluating reasoning that avoid saturation by design, and that allow testing models for other skills like creativity and task creation alongside problem solving.