“LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-Based Representations”, Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias B. Khalil2023-05-26 (GPT-4 nonfiction, IQ)⁠:

Can a Large Language Model (LLM) solve simple abstract reasoning problems? We explore this broad question through a systematic analysis of GPT-4 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 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 improve its reasoning ability.

Visualizations, GPT logs, and data are available at https://khalil-research.github.io/LLM4ARC/.