aiqbench analysis
KinshipQA introduces a generative pipeline for creating culture-specific genealogical data, enabling rigorous evaluation of large language models on multi-hop reasoning tasks involving implicit relational chains. By systematically varying task difficulty and cultural assumptions across six state-of-the-art models, the benchmark reveals significant performance gaps in how different architectures handle complex kinship inference. This work advances the field by providing a standardized, scalable dataset that isolates reasoning capabilities from general knowledge retrieval.
Kinship Data Benchmark for Multi-hop Reasoning
arXiv:2601.07794v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of information into a coherent inference. We introduce KinshipQA, a benchmark designed to probe this capability through reasoning over kinship relations. The central contribution of our work is a generative pipeline that produces, on demand, large-scale, realistic, and culture-specific genealogical data: collections of interconnected family trees that satisfy explicit marriage constraints associated with different kinship systems. This allows task difficulty, cultural assumptions, and relational depth to be systematically controlled and varied. From these genealogies, we derive textual inference tasks that require reasoning over implicit relational chains. We evaluate the resulting benchmark using six state-of-the-art LLMs, spanning both open-source and closed-source models, under a uniform zero-shot protocol with deterministic decoding. Performance is measured using exact-match and set-based metrics. Our results demonstrate that KinshipQA yields a wide spread of outcomes and exposes systematic differences in multi-hop reasoning across models and cultural settings.
Abstract:Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of information into a coherent inference. We introduce KinshipQA, a benchmark designed to probe this capability through reasoning over kinship relations. The central contribution of our work is a generative pipeline that produces, on demand, large-scale, realistic, and culture-specific genealogical data: collections of interconnected family trees that satisfy explicit marriage constraints associated with different kinship systems. This allows task difficulty, cultural assumptions, and relational depth to be systematically controlled and varied. From these genealogies, we derive textual inference tasks that require reasoning over implicit relational chains. We evaluate the resulting benchmark using six state-of-the-art LLMs, spanning both open-source and closed-source models, under a uniform zero-shot protocol with deterministic decoding. Performance is measured using exact-match and set-based metrics. Our results demonstrate that KinshipQA yields a wide spread of outcomes and exposes systematic differences in multi-hop reasoning across models and cultural settings.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2601.07794 [cs.CL]
(or arXiv:2601.07794v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2601.07794 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 255 words
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