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The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain

Arseny Moskvichev, Victor Vikram Odouard, Melanie Mitchell

TL;DR

ConceptARC introduces a concept-centered benchmark for the ARC domain to measure abstract understanding and generalization. By organizing 16 concept groups with varied task instantiations, the authors compare human performance to two top ARC-Kaggle solvers and GPT-4, finding a substantial human advantage and revealing generalization gaps in AI methods. The study analyzes error types and discusses limitations, proposing future directions such as language-guided training, hidden evaluation sets, and multimodal AI to advance conceptual abstraction. Overall, ConceptARC provides a framework to systematically evaluate and spur progress in AI's ability to learn and transfer abstract concepts beyond pattern recognition.

Abstract

The abilities to form and abstract concepts is key to human intelligence, but such abilities remain lacking in state-of-the-art AI systems. There has been substantial research on conceptual abstraction in AI, particularly using idealized domains such as Raven's Progressive Matrices and Bongard problems, but even when AI systems succeed on such problems, the systems are rarely evaluated in depth to see if they have actually grasped the concepts they are meant to capture. In this paper we describe an in-depth evaluation benchmark for the Abstraction and Reasoning Corpus (ARC), a collection of few-shot abstraction and analogy problems developed by Chollet [2019]. In particular, we describe ConceptARC, a new, publicly available benchmark in the ARC domain that systematically assesses abstraction and generalization abilities on a number of basic spatial and semantic concepts. ConceptARC differs from the original ARC dataset in that it is specifically organized around "concept groups" -- sets of problems that focus on specific concepts and that are vary in complexity and level of abstraction. We report results on testing humans on this benchmark as well as three machine solvers: the top two programs from a 2021 ARC competition and OpenAI's GPT-4. Our results show that humans substantially outperform the machine solvers on this benchmark, showing abilities to abstract and generalize concepts that are not yet captured by AI systems. We believe that this benchmark will spur improvements in the development of AI systems for conceptual abstraction and in the effective evaluation of such systems.

The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain

TL;DR

ConceptARC introduces a concept-centered benchmark for the ARC domain to measure abstract understanding and generalization. By organizing 16 concept groups with varied task instantiations, the authors compare human performance to two top ARC-Kaggle solvers and GPT-4, finding a substantial human advantage and revealing generalization gaps in AI methods. The study analyzes error types and discusses limitations, proposing future directions such as language-guided training, hidden evaluation sets, and multimodal AI to advance conceptual abstraction. Overall, ConceptARC provides a framework to systematically evaluate and spur progress in AI's ability to learn and transfer abstract concepts beyond pattern recognition.

Abstract

The abilities to form and abstract concepts is key to human intelligence, but such abilities remain lacking in state-of-the-art AI systems. There has been substantial research on conceptual abstraction in AI, particularly using idealized domains such as Raven's Progressive Matrices and Bongard problems, but even when AI systems succeed on such problems, the systems are rarely evaluated in depth to see if they have actually grasped the concepts they are meant to capture. In this paper we describe an in-depth evaluation benchmark for the Abstraction and Reasoning Corpus (ARC), a collection of few-shot abstraction and analogy problems developed by Chollet [2019]. In particular, we describe ConceptARC, a new, publicly available benchmark in the ARC domain that systematically assesses abstraction and generalization abilities on a number of basic spatial and semantic concepts. ConceptARC differs from the original ARC dataset in that it is specifically organized around "concept groups" -- sets of problems that focus on specific concepts and that are vary in complexity and level of abstraction. We report results on testing humans on this benchmark as well as three machine solvers: the top two programs from a 2021 ARC competition and OpenAI's GPT-4. Our results show that humans substantially outperform the machine solvers on this benchmark, showing abilities to abstract and generalize concepts that are not yet captured by AI systems. We believe that this benchmark will spur improvements in the development of AI systems for conceptual abstraction and in the effective evaluation of such systems.
Paper Structure (18 sections, 4 figures, 1 table)

This paper contains 18 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. The Abstraction and Reasoning Corpus
  3. The ConceptARC Benchmark
  4. Human and Machine Performance on ConceptARC
  5. Details of Human Studies
  6. Procedure
  7. Exclusion criteria
  8. Participants
  9. Details of Testing Winning Programs From the ARC-Kaggle Challenge
  10. Details of Testing GPT-4
  11. Discussion
  12. Human and Machine Performance
  13. Comparing Human and Machine Errors
  14. Limitations of Our Studies
  15. Related Work
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Three sample ARC tasks from ARC-Github. Each task consists of a set of task demonstrations---transformations between colored grids that follow the same abstract rule---and (here) a single test input. The job of the solver is to generate a new grid that results from applying the abstract rule to the test input. (Best viewed in color.)
  • Figure 2: Three sample tasks from ConceptARC, each of which has a set of demonstrations and three test inputs. Each task is a variation on the concept Sameness. (Best viewed in color.)
  • Figure 3: (a) ConceptARC task. (b) Corresponding prompt given to GPT-4. (Best viewed in color.)
  • Figure 4: Two examples illustrating human "near-miss" errors, compared with errors made by the first-place ARC-Kaggle program on the same test input. (a) A task in the Copy concept group. The human correctly copied the green and red object into the blue rectangle, but incorrectly deleted the original object. The first-place program ("Program") did not seem to grasp the notion of copying an object. (b) A task in the Extend To Boundary concept group. The human correctly extended a line to the boundary, but modified the original object to make it a solid rectangle rather than a single line. The first-place program did not seem to grasp the notion of extending a line from a given object to a boundary. (Best viewed in color.)