Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning
This work addresses the problem of assessing and improving abstract reasoning capabilities in language models for AI researchers, highlighting incremental progress through prompt engineering.
The paper systematically evaluates abstract common-sense reasoning in large language models using ConceptNet, finding that while models show consistent performance in ranking multiple relations, accuracy declines sharply when predicting a single relation, and few-shot prompting improves with selective retrieval but reveals biases.
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.