Neural networks for abstraction and reasoning: Towards broad generalization in machines
(2402.03507)Abstract
For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive.In this work, we look at several novel approaches for solving the Abstraction & Reasoning Corpus (ARC), a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks and rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation.We also propose a new encoding and augmentation scheme that allows LLMs to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining models to achieve better results than any system alone. Finally, we publish the arckit Python library to make future research on ARC easier.
Overview
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The paper explores novel neural network approaches to solve the Abstraction and Reasoning Corpus (ARC) tasks, which are benchmarks for machine generalization and abstract reasoning.
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It details the adaptation of the DreamCoder algorithm with the new Perceptual Abstraction & Reasoning Language (PeARL) and the application of LLMs to ARC tasks.
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An ensemble approach combining various models to improve overall performance on ARC tasks is proposed, alongside the introduction of the 'arckit' library to aid further research.
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The paper reveals that despite advancements, most advanced ML models solve only a fraction of ARC tasks, but highlights the complementary nature of different models as a pathway to improved performance.
Neural Networks for Abstraction and Reasoning: Towards Broad Generalization in Machines
Introduction to ARC Challenges
The field of AI has long pursued the goal of fostering broad generalization in machines—enabling them to learn new concepts from a minimal set of examples in ways akin to human cognition. The Abstraction and Reasoning Corpus (ARC), designed to measure such capabilities, presents abstract visual reasoning tasks requiring broad generalization beyond the training data. Despite significant interest and efforts, including international competitions, solving ARC tasks remains a formidable challenge, with state-of-the-art algorithms either failing to tackle the majority of ARC tasks or resorting to complex hand-crafted rules without leveraging ML.
Novel Approaches Examined
This paper investigates two novel approaches to tackling ARC tasks through recent advancements in neural networks. Firstly, the adaptation of the DreamCoder algorithm demonstrates significant strides, with the introduction of the Perceptual Abstraction & Reasoning Language (PeARL) enhancing DreamCoder's capabilities. Secondly, a novel methodology enabling LLMs to address ARC tasks reveals that the most advanced models can solve certain problems, indicating a complementary pathway to traditional solvers.
Ensemble Analysis and arckit Library
Beyond individual model performance, this study proposes an ensemble approach, combining various models to surpass the capabilities of any single system. This methodology not only highlights the heterogeneity of the ARC challenges but also underpins the creation of the arckit Python library, designed to facilitate future research in this vein.
Reflections on Performance
Performance analysis sheds light on the significant challenge ARC poses, with even the most advanced ML models solving only a fraction of the tasks. Despite this, the paper's findings underscore the effectiveness of DreamCoder and its enhancements, achieving a 16.5% accuracy on ARC-Easy and 4.5% on ARC-Hard tasks, marking a notable improvement over previous implementations. The investigation into LLMs further contributes to this discourse, showcasing their potential despite the inherent limitations of current models.
Findings on Model Complementarity
A particularly compelling aspect of this research is the discovery of the complementary nature of different models in tackling ARC tasks. The low overlap in solved tasks between models like DreamCoder and GPT-4 suggests that an ensemble approach, leveraging the diverse strengths of various algorithms, could dramatically improve overall performance.
Future Directions
The ongoing challenge ARC presents invites further exploration, with potential avenues including the extension of DreamCoder to incorporate more complex DSL features, the application of LLMs through advanced prompting techniques, and the exploration of Large Vision Models for visual reasoning. Additionally, the impending release of ARC2 promises to heighten the benchmark for broad generalization in AI, encouraging continued innovation in this field.
Conclusion
In conclusion, this study not only propels our understanding of ML models' capabilities and limitations in abstract reasoning and generalization but also provides a robust framework for future research. By embracing the complexity of ARC tasks and exploring novel methodologies and ensemble approaches, this research moves us closer to achieving the goal of broad generalization in machines—a cornerstone in the journey toward advanced artificial intelligence.
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