MemeCULT-1K is a multilingual benchmark of 1,000 South Asian memes in Bengali, English, and Hindi, each paired with a cultural context note and three human-written explanations, plus an additional set of 54 Bengali regional dialect memes. The study evaluates thirteen vision‑language models under meme‑only and context‑aware settings, showing that providing minimal cultural context consistently improves performance across all models and languages. Error analysis indicates closed‑source models struggle with entity and reference misidentification, while open‑source models are limited by broader cultural knowledge gaps, especially in linguistic and phonological aspects.
By Tawsif Tashwar Dipto, Mehedi Ahamed, Radib Bin Kabir, Mueeze Al Mushabbir, Mohammed Saidul Islam, Mir Rayat Imtiaz Hossain, Md Tahmid Rahman Laskar, Sabbir Ahmed
arXiv:2606. 17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested.
By Thennal DK, Hans Ole Hatzel
arXiv:2608. 02486v1 Announce Type: cross Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or Chinese mythology far less consistently.
By Iaroslav Chelombitko, Ekaterina Chelombitko, Mika H\"am\"al\"ainen
arXiv:2607. 24276v1 Announce Type: cross Abstract: Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words.
By Priyansh Srivastava
The paper introduces a multilingual story moral generation task to evaluate cultural alignment in large language models. Using a dataset of human-written story morals from 14 language‑culture pairs, the authors compare model outputs to human interpretations through semantic similarity, a preference survey, and value categorization. They find that advanced models like GPT‑4o and Gemini produce morally similar and preferred responses but show less cross‑linguistic variation, focusing on a narrower set of shared values, indicating a limitation in capturing the diversity of human narrative understanding.
By Sophie Wu, Andrew Piper
The paper introduces a generation benchmark for culturally specific kinship terms, evaluating five open‑weight large language models (LLMs) on Hindi, Tamil, and Korean. Unlike prior multiple‑choice tests that treat kinship understanding as a recognition task, the study prompts LLMs to generate terms across two communicative tasks and compares results to a matched option‑supported baseline. Findings show that while models like GPT‑OSS120B and Llama‑3.370B can select correct terms in over 90% and 78% of cases respectively, they produce the correct term only 36% and 24% of the time, indicating a significant evaluation‑format gap and highlighting the difficulty of culturally specific kinship generation even when relationships are explicitly stated.
By Sahil Pardasani, Madhusudan Singh
Large language models (LLMs) process text through subword tokenizers rather than directly reading characters or words. Because these tokenizers are trained predominantly on English-centric corpora, they introduce a systematic and often overlooked disadvantage for many non-English languages.
The paper investigates how multilingual large language models perform when generating stories in Urdu, a low‑resource language. The authors created a corpus of 93 Urdu stories produced by GPT‑5.1, Qwen‑3‑Max, and DeepSeek‑3.1, and manually annotated errors across a nine‑label taxonomy covering linguistic, semantic, and cultural aspects. Findings reveal frequent grammatical and semantic mistakes, lack of coherence, unnatural repetition, and pervasive cultural shallowness, with few‑shot prompting failing to resolve many of these issues.
By Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt, Hassan Sajjad
arXiv:2606.11009v2 Announce Type: replace
Abstract: Large language models are increasingly used to adapt math word problems for personalized learning at scale, but it remains an open question whether...
By Parisa Suchdev, Juniper Lovato
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 weig...
arXiv:2607. 06544v1 Announce Type: new Abstract: As Artificial Intelligence (AI) makes inroads into different parts of the Indian subcontinent, there is significant interest in studying how AI impacts the linguistic and cultural foundations of this civilization.
By Aparna Madva, Sharath Srivatsa, Srinath Srinivasa, Tulika Saha
Viveka-Insight is a bilingual resource and open‑source pipeline for Swami Vivekananda’s complete works, providing a structure‑preserving parse of 32,694 paragraphs and 168,842 sentences, a cross‑lingual concept graph with 8,362 language‑agnostic concepts, a bilingual alias inventory of 60,850 surface forms, and a human‑annotated set of 200 paragraph‑concept edges. The resource enables citation‑grounded retrieval across the English and Bengali corpora, achieving Recall@10 of 0.86 for known‑item cross‑lingual queries and demonstrating concept‑extraction precision of 0.60 (up to 0.71 with confidence filtering). The design is intended to be transferable to other multilingual classical corpora.
By Tamal Maharaj