Year: 2026
Type: conference-paper
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-OSS-120B selects the correct term in 90.67% of 75 valid cells but produces an accepted term in 36.00% of the corresponding attempts; Llama-3.3-70Bshows 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-3.3-70B 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.
Authors: Sahil Pardasani, Madhusudan Singh
Institution: Pennsylvania State University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7214229174