[Submitted on 22 Sep 2026]
Title:Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms
View a PDF of the paper titled Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms, by Sahil Pardasani and 1 other authors
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Abstract:Current literature evaluates large language models (LLMs) on multilingual kinship understanding using multiple choice benchmarks, treating it as a recognition problem. We instead prompt five open weight LLMs to generate kinship terms in three non Western languages (Hindi, Tamil, and Korean) across two communicative tasks and pair this with a matched option-supported selection baseline. On identical relation language cells, GPT OSS120B selects the correct term in 90.67% of 75 valid cells but produces an accepted term in 36.00% of the corresponding attempts; Llama 3.370B shows the same pattern (77.92% versus 24.24%). Since the four-option condition displays the candidate terms and does not require script production, the difference is interpreted as an evaluation format gap rather than direct proof that lexical knowledge is intact. On explicitly specified L3 prompts, accuracy varies sharply, from GLM-5.1 at 72.29% to Llama-3.370B at 24.24%. The paternal-lineage advantage is language specific; it is large in Hindi but weak or reversed in Korean, while Tamil shared-term pairs provide a control for measurement variation. These results show that culturally specific kinship generation remains difficult even when the relationship is explicitly stated and motivate generation-based evaluation alongside multiple-choice testing.
Comments: Accepted at (ORACLE Workshop), EMNLP 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.26942 [cs.CL]
(or arXiv:2609.26942v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.26942
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Madhusudan Singh Ph.D. [view email] [v1] Tue, 22 Sep 2026 18:30:09 UTC (29 KB)
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