Beyond the hype: Why AI still falls short in complex analogy tasks

While LLMs have achieved impressive results in many domains, their struggles with analogical reasoning suggest that they do not yet match human cognitive flexibility. The study emphasizes the need for more rigorous evaluations of AI models, particularly in assessing their ability to generalize reasoning beyond their training data.

Artificial intelligence has demonstrated remarkable capabilities in natural language processing, yet its ability to perform abstract reasoning remains a topic of debate. A recent study titled “Evaluating the Robustness of Analogical Reasoning in Large Language Models” by Martha Lewis and Melanie Mitchell, published in arXiv, challenges the assumption that large language models (LLMs) possess human-like analogical reasoning skills. By testing OpenAI’s GPT models on variants of analogy problems across different domains, the study uncovers significant limitations in the robustness of AI-generated reasoning, revealing its susceptibility to shortcuts and biases.

Limits of analogical reasoning in LLMs

Analogical reasoning - the ability to recognize patterns and apply relationships across different contexts - is a fundamental aspect of human cognition. This study examines how well LLMs can handle three types of analogy problems: letter-string analogies, digit matrices, and story-based analogies. The researchers aimed to determine whether AI models rely on genuine abstract reasoning or simply replicate patterns from their training data.

The study’s methodology involved testing human participants and several GPT models on original analogy problems, then assessing their performance on modified variants. If the AI models employed robust reasoning, their accuracy should remain stable across both original and modified tasks. However, the results indicated a sharp decline in LLMs' accuracy on variant problems, suggesting that their reasoning ability is not as generalizable as previously thought.

Key findings and implications

For letter-string analogies, the models performed well on standard problems but struggled when the letter sequences were altered using fictional alphabets or non-letter symbols. While humans maintained high accuracy across variations, GPT models' performance dropped significantly, indicating that their success on familiar tasks may stem from memorization rather than genuine reasoning. The study also found that GPT models were particularly sensitive to the presentation format, suggesting a reliance on pattern recognition rather than abstract rule application.

A similar pattern emerged in digit matrix problems, inspired by Raven’s Progressive Matrices. These tasks required identifying numerical patterns within a grid. When the standard digit format was altered - such as changing the position of the missing element or replacing numbers with symbols - human accuracy remained stable, while AI performance deteriorated. This highlights the models’ reliance on learned patterns rather than abstract cognitive processing. Furthermore, the AI models struggled to generalize solutions across different transformations, reinforcing concerns about their limited adaptability.

The study also tested story-based analogies, where participants had to determine which of two stories was most analogous to a reference story. Unlike humans, GPT models exhibited strong answer-order biases, favoring responses that appeared earlier in the prompt. Furthermore, when the correct analogy was paraphrased to reduce superficial similarities, the models’ accuracy declined, reinforcing concerns about their dependency on surface-level cues rather than deep reasoning structures. The findings suggest that AI models may lack an intrinsic understanding of causality, which is a crucial component of human analogy-making.

Future of AI in abstract reasoning

These findings raise critical questions about the capabilities of AI in complex problem-solving and reasoning. While LLMs have achieved impressive results in many domains, their struggles with analogical reasoning suggest that they do not yet match human cognitive flexibility. The study emphasizes the need for more rigorous evaluations of AI models, particularly in assessing their ability to generalize reasoning beyond their training data.

Future research should explore methods to improve AI’s robustness in reasoning tasks, such as incorporating metacognitive processes or designing models that can engage in deeper abstract thinking. Potential solutions could include integrating structured knowledge representations, enhancing training methodologies to focus on causal relationships, and developing hybrid AI systems that combine symbolic reasoning with neural architectures. Additionally, efforts should be made to reduce AI reliance on statistical shortcuts by creating more diverse and challenging training datasets.

As AI continues to evolve, understanding its cognitive limitations will be essential for developing more reliable and adaptable intelligent systems. The study underscores the importance of interdisciplinary research, combining cognitive science, linguistics, and artificial intelligence to create models that can truly replicate human-like reasoning. The long-term goal is not only to improve AI’s ability to reason by analogy but also to foster the development of AI that can engage in complex problem-solving in dynamic and unpredictable environments.