Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning

TL;DR

This paper evaluates large language models' ability to perform abstract common-sense reasoning using ConceptNet, revealing significant gaps compared to humans but also potential improvements through prompt engineering.

Contribution

It introduces two prompting methods for assessing common-sense reasoning in LLMs and systematically analyzes their performance and biases using ConceptNet.

Findings

Models perform well in relation ranking but poorly in single-relation prediction.

Few-shot prompting improves accuracy when selecting from limited relations.

Prompt engineering and selective retrieval can enhance reasoning performance.

Abstract

Large language models (LLMs) have achieved remarkable performance in generating human-like text and solving reasoning tasks of moderate complexity, such as question-answering and mathematical problem-solving. However, their capabilities in tasks requiring deeper cognitive skills, such as common-sense understanding and abstract reasoning, remain under-explored. In this paper, we systematically evaluate abstract common-sense reasoning in LLMs using the ConceptNet knowledge graph. We propose two prompting approaches: instruct prompting, where models predict plausible semantic relationships based on provided definitions, and few-shot prompting, where models identify relations using examples as guidance. Our experiments with the gpt-4o-mini model show that in instruct prompting, consistent performance is obtained when ranking multiple relations but with substantial decline when the model is restricted to predicting only one relation. In few-shot prompting, the model's accuracy improves significantly when selecting from five relations rather than the full set, although with notable bias toward certain relations. These results suggest significant gaps still, even in commercially used LLMs' abstract common-sense reasoning abilities, compared to human-level understanding. However, the findings also highlight the promise of careful prompt engineering, based on selective retrieval, for obtaining better performance.