arXiv AI
Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities
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arXiv Computation and Language
Sep 25
Language Specific Knowledge: Do Models Know Better in X than in English?
The paper introduces the concept of Language Specific Knowledge (LSK), showing that multilingual language models can answer certain queries better when prompted in a language other than English, sometimes even in low‑resource languages. It defines a language‑selection problem and presents several baseline methods, including the authors’ LSKExtractor, to empirically demonstrate that choosing the optimal language can improve question‑answering performance across datasets covering cultural and social norms. Experiments reveal non‑intuitive mappings, such as Gemma models excelling on Chinese and Middle Eastern topics in Spanish and Qwen models performing best on authority and responsibility queries in Arabic and Chinese.
arXiv Computation and Language
Sep 15
Identifying Crucial Attention Heads for Multilingual Language Models: Retrieval and Retrieval-Transition Heads
arXiv:2602.22453v4 Announce Type: replace Abstract: Retrieval heads, a subset of attention heads in Transformers, were studied in English, showing its crucial role in retrieving information from the...
arXiv AI
Jun 8
The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs
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.
Hugging Face Trending Papers
Jul 23
When Trivia Is Not Trivial: Everyday Knowledge Failures in Multilingual LLMs
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 AI
3d ago
Linguistic Loopholes in LLM Unlearning: From a 174-Language Benchmark to Coverage-Aware Unlearning
The paper introduces the problem of cross‑lingual loopholes in large language model (LLM) unlearning, where forgetting a fact in one language can leave it accessible in others. It presents a new 174‑language benchmark, the Cross‑Lingual Unlearning Tensor, and proposes COVER, a method that selects a subset of source languages to maximize unlearning coverage under a language budget. Experiments show COVER reduces residual knowledge by 7.8–27.3% compared to uniform selection and works on both synthetic and real low‑resource news data.
arXiv Computation and Language
Sep 17
M-SQE: Multilingual Skill Quality Estimation for Enhancing Language Equality in Agentic Skill Use
M‑SQE is a post‑retrieval framework that estimates the quality of multilingual agent skills by combining a Theory view (intrinsic quality) and an Action view (task‑grounded utility) into a domain‑conditioned score. It was evaluated on general, tool‑use, and cultural skill‑use domains, showing a task‑success improvement of at least +3.5 points over baselines across three retrievers. The method notably boosts performance for low‑resource languages, raising Hindi by +12.9 pp and Swahili by +5.6 pp, and achieves strong results across six cultural regions, advancing linguistic and cultural equality in agentic skill use.