LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and
the Importance of Object-based Representations
- URL: http://arxiv.org/abs/2305.18354v2
- Date: Wed, 14 Feb 2024 21:15:31 GMT
- Title: LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and
the Importance of Object-based Representations
- Authors: Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias B.
Khalil
- Abstract summary: We show that GPT-4 is unable to "reason" perfectly within non-language domains such as the 1D-ARC or a simple ARC subset.
We propose an object-based representation that is obtained through an external tool, resulting in nearly doubling the performance on solved ARC tasks and near-perfect scores on the easier 1D-ARC.
- Score: 50.431003245201644
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Can a Large Language Model (LLM) solve simple abstract reasoning problems? We
explore this broad question through a systematic analysis of GPT on the
Abstraction and Reasoning Corpus (ARC), a representative benchmark of abstract
reasoning ability from limited examples in which solutions require some "core
knowledge" of concepts such as objects, goal states, counting, and basic
geometry. GPT-4 solves only 13/50 of the most straightforward ARC tasks when
using textual encodings for their two-dimensional input-output grids. Our
failure analysis reveals that GPT-4's capacity to identify objects and reason
about them is significantly influenced by the sequential nature of the text
that represents an object within a text encoding of a task. To test this
hypothesis, we design a new benchmark, the 1D-ARC, which consists of
one-dimensional (array-like) tasks that are more conducive to GPT-based
reasoning, and where it indeed performs better than on the (2D) ARC. To
alleviate this issue, we propose an object-based representation that is
obtained through an external tool, resulting in nearly doubling the performance
on solved ARC tasks and near-perfect scores on the easier 1D-ARC. Although the
state-of-the-art GPT-4 is unable to "reason" perfectly within non-language
domains such as the 1D-ARC or a simple ARC subset, our study reveals that the
use of object-based representations can significantly improve its reasoning
ability. Visualizations, GPT logs, and data are available at
https://khalil-research.github.io/LLM4ARC.
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