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LLMs struggle with kinship reasoning when relations are presented abstractly

By PulseAugur Editorial · [1 sources] ·

A new research paper explores how large language models handle kinship reasoning tasks, finding that their performance is significantly impacted by how relations are presented. Models like Qwen3.8-27B and Gemma 4 - 26B-A4B performed substantially better when relations were described using familiar vocabulary compared to explicitly defined nonce predicates. While reasoning budgets and prompt interventions can mitigate this gap, the study concludes that LLMs' manifested relational competence is not indifferent to presentation, indicating a preference for learned linguistic associations over formal definitions. AI

IMPACT Highlights the importance of prompt engineering and data presentation for LLM reasoning capabilities.

RANK_REASON Academic paper detailing model performance on a specific reasoning task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with kinship reasoning when relations are presented abstractly

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Academic paper detailing model performance on a specific reasoning task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Thomas Pashby ·

    The Concrete-Arbitrary Gap: Kinship Reasoning in LLMs Is Not Indifferent to Presentation

    arXiv:2609.39913v1 Announce Type: new Abstract: We test whether large language models solve formally matched kinship problems equally well when relations are expressed in familiar vocabulary or by explicitly defined nonce predicates. Across 500 paired graphs, concrete accuracy ex…

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