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About this paper
Junjie Wu, Mo Yu, Lemao Liu, Dit-Yan Yeung, Jie Zhou
A study investigates the underlying reasons for Large Language Models' fluid intelligence deficiency using the Abstraction and Reasoning Challenge (ARC) benchmark. The research reveals that even leading models like GPT-4o achieve only 19% accuracy on ARC tasks, pointing to limitations in compositional reasoning, abstract input understanding, and architectural biases.
Key insights
- Large Language Models (LLMs) struggle with even basic geometric and spatial 'atomic operations' and exhibit a fundamental deficiency in compositional reasoning.
- The way abstract, non-linguistic inputs (like grid data) are represented significantly impacts LLM performance; converting them to natural language descriptions can substantially improve results.
- Intrinsic modeling limitations, such as left-to-right autoregressive decoding, and an inability to correctly identify relevant information from in-context examples, contribute to LLMs' fluid intelligence deficiencies.
Summary by alphaXiv.
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