arXiv:2606. 07422v1 Announce Type: cross Abstract: Large language models are increasingly used to answer culturally grounded questions across languages, yet it remains unclear whether local cultural knowledge is better accessed through English or the local language.
By Yang Zhang, Xiao Fei, Amr Mohamed, Sarah Almeida Carneiro, Mersin Konomi, Mingmeng Geng, Ahmed Asaad, Guokan Shang, Michalis Vazirgiannis
arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.
By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke
Quiz rooms, trivia nights, and quiz shows challenge human knowledge across a wide range of topics, from canonical facts to everyday culture. In this paper, we examine whether large language models (LLMs) can perform competitively in such settings, using quiz-style questions to test them on both common and niche topics.
arXiv:2607. 06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing research has predominantly focused on English.
By Andrea Alfarano, Andrea Bacciu, Saab Mansour, Amin Mantrach, Marcello Federico
The paper presents a unified evaluation of cross‑lingual consistency (CLC) enhancement methods for multilingual language models, covering inference‑time interventions and post‑training approaches across three model families and three closed‑form benchmarks. Results indicate that post‑training methods, especially direct distribution alignment, consistently improve CLC across all model‑dataset combinations, while other methods are more sensitive to answer format and language coverage. The study also examines the impact of CLC enhancement on culturally diverse question answering, finding no systematic degradation in controlled settings but occasional accuracy drops in open‑ended generation, particularly for non‑English responses.
By Jirui Qi, Mingyang Wang, Hinrich Sch\"utze, Raquel Fern\'andez, Arianna Bisazza
The paper compares Knowledge-Graph Based Augmentation (Graph-RAG) with Retrieval-Augmented Generation (RAG) for answering culturally specific questions. Using the LatamQA dataset, Graph-RAG, built automatically from Wikipedia via KGGen, matches RAG performance and reduces the base LLM’s error by 72% with a standard KG and 78% with a benchmark-aware variant. The approach also transfers zero‑shot to Portuguese, showing multilingual applicability.
By Pablo Poulenard, Yannis Karmim, Valentin Barri\`ere